รปแบบของการจดการขอมลในองคกร (Formats of Data Management in Organizations)

- กระดาษ (Paper)

ขอมลทอยในรปของกระดาษ ทาใหไมสามารถประมวลผลขอมลโดยโปรแกรมคอมพวเตอรได อกทampงยampงย(งยากและเสยเน)อทมากในการจampดเก+บขอมล- แฟมขอมล (Files)

ขอมลทอยในรปของแฟมขอมลคอมพวเตอร ทาใหสามารถประมวลผลขอมลดวยโปรแกรมคอมพวเตอรได การจampดเก+บขอมลทาไดงายขนและยampงชวยประหยampดเน)อทในการจampดเก+บขอมลแตก+ยampงมป1ญหาเน)องจากแฟมขอมลทมอยจานวนมากนampน ไมมการจampดการจampดเก+บรวมกampนอยางเป3นระบบ-ฐานขอมล (Databases)

ขอมลทอยในรปของฐานขอมล มการจampดการขอมลอยางเป3นระบบโดยการจampดการขอมลตางๆ

ทเดมอยอยางกระจampดกระจายในแฟมขอมล โดยนามาจampดเก+บรวมกampนอยางเป3นระบบไวในฐานขอมลเดยวกampน มการจampดการพจนาน(กรมขอมล (Data Dictionary

หร)อ Metadata) ทาใหงายในการปรampบปร(งขอมล การประมวลผล และการเรยกใชขอมล รวมทampงยampงมการรampกษาความปลอดภampยของขอมลดวย แตฐานขอมลก+ยampงไมเหมาะในการเรยกใช (Retrieve) และวเคราะหขอมล (Analyze) โดยผบรหาร

- คลampงขอมล (Data Warehouse)

ขอมลทอยในรปของคลampงขอมล มการจampดการขอมลใหเหมาะแกการเรยกใชและวเคราะหขอมลโดยผบรหาร ขอมลในคลampงขอมลจะถกจampดเก+บตามเร)อง (Subject)

ทผบรหารตองการวเคราะห โดยในแตละเร)องสามารถทาการวเคราะหไดในหลากหลายม(มมอง (Multi-Dimensions)

มารทขอมล (Data Marts)

มารทขอมล ค)อ คลampงขอมลยอยๆของระดampบแผนก ในองคกรอาจจะมคลampงขอมลขององคกร

และในแตละแผนกอาจจะมมารทขอมลของแผนกนampนๆดวย

คลงขอมลคออะไร (What Is Data Warehouse)A data warehouse is a subject-oriented integrated

time-variant andnonvolatile collection of data in support of managementrsquos decision makingprocesshellip W H Inmon

Subject oriented There is a shift from application-oriented data (ie data designed to support application processing) to decision-support data (ie data designed to aid in decision making) If designed well subject-oriented data provides a stable image of business processes independent of legacy systems In

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

ทเดมอยอยางกระจampดกระจายในแฟมขอมล โดยนามาจampดเก+บรวมกampนอยางเป3นระบบไวในฐานขอมลเดยวกampน มการจampดการพจนาน(กรมขอมล (Data Dictionary

หร)อ Metadata) ทาใหงายในการปรampบปร(งขอมล การประมวลผล และการเรยกใชขอมล รวมทampงยampงมการรampกษาความปลอดภampยของขอมลดวย แตฐานขอมลก+ยampงไมเหมาะในการเรยกใช (Retrieve) และวเคราะหขอมล (Analyze) โดยผบรหาร

- คลampงขอมล (Data Warehouse)

ขอมลทอยในรปของคลampงขอมล มการจampดการขอมลใหเหมาะแกการเรยกใชและวเคราะหขอมลโดยผบรหาร ขอมลในคลampงขอมลจะถกจampดเก+บตามเร)อง (Subject)

ทผบรหารตองการวเคราะห โดยในแตละเร)องสามารถทาการวเคราะหไดในหลากหลายม(มมอง (Multi-Dimensions)

มารทขอมล (Data Marts)

มารทขอมล ค)อ คลampงขอมลยอยๆของระดampบแผนก ในองคกรอาจจะมคลampงขอมลขององคกร

และในแตละแผนกอาจจะมมารทขอมลของแผนกนampนๆดวย

คลงขอมลคออะไร (What Is Data Warehouse)A data warehouse is a subject-oriented integrated

time-variant andnonvolatile collection of data in support of managementrsquos decision makingprocesshellip W H Inmon

Subject oriented There is a shift from application-oriented data (ie data designed to support application processing) to decision-support data (ie data designed to aid in decision making) If designed well subject-oriented data provides a stable image of business processes independent of legacy systems In

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

และในแตละแผนกอาจจะมมารทขอมลของแผนกนampนๆดวย

คลงขอมลคออะไร (What Is Data Warehouse)A data warehouse is a subject-oriented integrated

time-variant andnonvolatile collection of data in support of managementrsquos decision makingprocesshellip W H Inmon

Subject oriented There is a shift from application-oriented data (ie data designed to support application processing) to decision-support data (ie data designed to aid in decision making) If designed well subject-oriented data provides a stable image of business processes independent of legacy systems In

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

คลงขอมลคออะไร (What Is Data Warehouse)A data warehouse is a subject-oriented integrated

time-variant andnonvolatile collection of data in support of managementrsquos decision makingprocesshellip W H Inmon

Subject oriented There is a shift from application-oriented data (ie data designed to support application processing) to decision-support data (ie data designed to aid in decision making) If designed well subject-oriented data provides a stable image of business processes independent of legacy systems In

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Subject oriented There is a shift from application-oriented data (ie data designed to support application processing) to decision-support data (ie data designed to aid in decision making) If designed well subject-oriented data provides a stable image of business processes independent of legacy systems In

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

other words it captures the basic nature of the business environmentIntegrated The database consolidates application data from different legacy systems which use different encoding measurement units and so on and eliminates inconsistencies in the dataTime-variant Informational data has a time dimension each data point is associated with apoint in time and data points can be compared along that time axis unlike operational datawhich is valid only at the moment of access capturing a moment in timeNonvolatile New data is always appended rather than replaced The database continuallyabsorbs new data integrating it with the previous data

คลงขอมล vs มารทขอมล (Data Warehouses vs Data Marts)

ฐานขอมลปฏบตงาน vs คลงขอมล(Operational Database vs Data Warehouse)

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

ระดบการใชขอมลสารสนเทศของผใช (Levels of DataInformation Usage)

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

คณภาพของขอมลสารสนเทศ (Quality of

DataInformation) 1048715 ตรงตามวampตถ(ประสงคของผใช (Objective)

1048715 มาจากแหลงขอมลทเช)อถ)อได (Source)

1048715 ความถกตองของขอมลสารสนเทศ (Accuracy)

1048715 ความทampนสมampยของขอมลสารสนเทศ (Currency)

1048715 ความครบถวนของขอมลสารสนเทศ (CoverageCompleteness)1048715 มคาอธบายหร)อพจนาน(กรมขอมลทชampดเจน (DefinitionData Dictionary)1048715 มความเป3นมาตราฐาน (Standard)

ห(วงโซ(มลค(าการจดการขอมล(Data Management Value Chain)

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

วงจรขอมล-สารสนเทศ-การตดสนใจ (DatandashInformationndashDecision Cycle)

ธรกจอจฉรยะคออะไร (What is Business Intelligence)Business Intelligence is a process for increasing

the competitive advantage of a business by intelligent use of available data in decision making It is about access analysis and uncovering new opportunitiesrdquo

ldquoธ(รกจอampจฉรยะ (BI Business Intelligence) ค)อ กระบวนการสาหรampบเพมความไดเปรยบในการแขงขampนของธ(รกจโดยการใชขอมลทมอย

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

ไดอยางอampจฉรยะ ธ(รกจอampจฉรยะ ค)อ การเขาถงการวเคราะห และการคนพบโอกาสใหมๆ rdquo

Financial Intelligence1048715 Financial Intelligence provides your finance managers with the ability to evaluate and improve financial performance review a wide array of financial activities and optimize internal processes1048715 Financial Intelligence will provide your organization with a comprehensive report on its financial health covering everything from the profit and loss sheet revenue plan and forecast balance sheet and general ledger1048715 By managing your revenue cycle you can make informed cash and revenue forecasts Plus you can

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

ensure that your decisions contribute to strong business performanceCustomer Intelligence1048715 While itrsquos important to retain customers itrsquos even more important to retain the rightcustomers But how do you know who the right customers are1048715 The Customer Intelligence makes it possible for your organization to identify yourmost profitable customers optimize sales activities around those customersidentify successful marketing campaigns and take action1048715 In addition the application gives managers insight into call center activity andchanging customer behavior allowing them to take action before itrsquos too late

Product and Service Intelligence1048715 Optimizing product profitability requires you to make decisions based on a varietyof factors including product mix and pricing product promotions sales channelperformance and customer behavior1048715 Product and Service Intelligence provides key insights into product behavior thatimpacts brand strategy promotional and merchandising mix and product lifecyclemanagement issues1048715 Product and Service Intelligence allows you to optimize product profitability andidentify cross-sell and up-sell opportunitiesSupply Chain Intelligence

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Flexibility responsiveness and reliability are the critical elements that make up asuccessful supply chain in todayrsquos marketplace1048715 Supply Chain Intelligence allows you to evaluate monitor and improve yoursupply chain performance and efficiency1048715 With this application you can improve supplier management increasemanufacturing efficiency optimize delivery and return management and more1048715 Supply Chain Intelligence provides key performance indicator (KPI) benchmarksand best practice methodologies to help your users analyze supply chain cycletimes inventory holding costs and demand forecastsHuman Resource Intelligence1048715 Organizations are faced with more complex employee issues than ever before from designing compensation and incentive plans around the companyrsquos highestperformers to dealing with independent contractors foreign visa requirements anda myriad of payroll and federal reporting requirements1048715 Human Resource Intelligence can provide insights to your human resources (HR)managers by supplying the answers to help you maximize employee recruitmentretention and results1048715 Whether they are dealing with employee retention and turnover rates or salescommissions and bonus payouts your HR team needs to have confidence in thesecurity confidentiality and accuracy of their data

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Human Resource Intelligence allows your HR managers to access the data theyneed when they need it so they can make highly informed personnel decisions

Financial Intelligence Areas1048715 การวเคราะหประสทธภาพทางการเงน (Financial Performance Analytics)1048715 การวเคราะหตนท(น (Cost Analytics)

1048715 การวเคราะหคาใชจาย (Expense Analytics)

1048715 การวเคราะหรายได (Revenue Analytics)

1048715 การวเคราะหบampญชลกหน (Accounts Receivable Analytics)

1048715 การวเคราะหบampญชเจาหน (Accounts Payable Analytics)

Dimensions1048715 Time Dimension1048715 Person Dimension1048715 Sale Channel Dimension1048715 Financial Category Dimension1048715 Line of Business Dimension1048715 Product Dimension1048715 Cost Center Dimension1048715 Profit Center Dimension

Measures1048715Profit and Loss1048715Turnover1048715Cost of sales1048715Gross profit1048715Operating expenses1048715Operating profit1048715Other costsincome

1048715Profit before interest and taxation1048715Net interest receivable (payable)1048715Profit on ordinary activities before taxation

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715Tax on profit on ordinary activities1048715Profit on ordinary activities after taxation1048715Equity minority interests

1048715Profit for the financial period1048715Dividends1048715Retained profit

1048715Balance Sheet1048715Intangible Assets1048715Tangible assets1048715Investments1048715Total Fixed Assets1048715Stock1048715Debtors due within one year1048715Short-term investments1048715Cash at bank and in hand1048715Total Current Assets1048715Creditors Amounts falling duewithin one year1048715Net current assets (liabilities)1048715Total assets less current liabilities

1048715Creditors Amounts falling dueafter more than one year1048715Provisions for liabilities andcharges1048715Net assets1048715Called-up share capital1048715Share premium1048715Other reserves1048715Profit and loss account1048715Equity shareholders funds1048715Minority interests1048715Total capital employed1048715Weighted average number ofshares in issue in the period

1048715 Others1048715 of Forecast1048715 Forecast vs Budget1048715 Expenses per Head1048715 TampE per Head1048715 Current Headcount

1048715 Invoice Entered1048715 Invoices Paid1048715 Paid Late1048715 Invoice to Payment Days

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Payments1048715 Discount Offered1048715 Discount Taken1048715 Open Payable Amount1048715 Invoices Due Amount1048715 Number Invoices Due1048715 Weighted Average Days Due1048715 Invoices Past Due Amount

1048715 Number Invoices Past Due1048715 Weighted Average Days Past Due1048715 Discount Remaining Amount1048715 Invoices on Hold Amount1048715 Invoices on Hold

1048715Financial Ratios1048715 Gross Profit Margin ()1048715 Operating Profit Margin ()1048715 Net Profit Margin ()1048715 Retained Profit Margin ()1048715 Profit Mark Up ()1048715 Profit Before Interest and Taxation ()1048715 Profit Before Taxation ()1048715 Profit for the Year ()1048715 Operation Cost ()1048715 Interest Cost ()

1048715 ROCE (Return On Equity Employed)()1048715 ROTA (Return On Total Asset) ()1048715 ROFA (Return On Fixed Asset) ()1048715 ROWC (Return On Working Capital) ()1048715 Current Ratio (Working Capital Ration)1048715 Quick Ratio (Acid Test Ratio)1048715 Total Asset Turnover1048715 Stock Turnover1048715 Debtorrsquos Turnover

1048715 Creditorrsquos Turnover1048715 Fixed Asset Turnover1048715 Current Asset Turnover1048715 Capital Employed Turnover

1048715 Working Capital Turnover1048715 Earning Per Share1048715 Dividend Per Share1048715 Dividend Yield1048715 Dividend Cover

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 PriceEarning1048715 EBIT1048715 EBITDA

ห(วงโซ(มลค(าของธรกจอจฉรยะ (Business Intelligence Value Chain)

ทาไมตองม0ธรกจอจฉรยะ (Why Business Intelligence)1048715 Business intelligence is fast becoming a strategic differentiator for todayrsquos leadingorganizations1048715 Managers need a consolidated view of their key enterprise metrics and performanceindicators in order to make intelligent decisions Business Intelligence can provideyour organization with the most comprehensive approach to analytics on the markettoday1048715 In todayrsquos competitive markets enterprises need to manage and reduce operational

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

costs One key benefit of BI is that it gives executives mid-level or line managersand employees the information they need to drive operational efficiencies1048715 BI is also a key factor in improving top line revenue growth As competitionincreases the ability to understand and target particular customer segments withappropriate and profitable products and services becomes a key differentiator BIhelps the drive towards higher service levels and increased revenues by bringing tolight the latest trends in customer behavior determining which customer segmentsare the most profitable and identifying cross-selling opportunities1048715 A BI strategy is a fundamental foundation for enterprise performance management(EPM) a process that connects goals metrics and people in order to drive improvedmanagement analysis and action across the organization1048715 IT budgets are tight But IT is still spending in some areas Business Intelligence(BI) is one of them Why Because BI projects1048715 Leverage existing information investments1048715 Are relatively low cost and low risk1048715 Deliver proven high return on investment1048715 Because of this the BI market continues to show continued strong growth This in turn means that most large organizations are in the process of initiating new BI projects

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

5 ข1นตอนของธรกจอจฉรยะ(The Five Key Stages of Business Intelligence)Data sourcing1048715 Business Intelligence is about extracting information from multiple sources of dataThe data might be text documents photographs and images sounds web pagesfiles database data mart and data warehouse1048715 The key to data sourcing is to obtain the information in electronic form

Data analysis1048715 Business Intelligence is about synthesizing useful knowledge from collections ofdata It is about estimating current trends integrating and summarizing disparateinformation validating models of understanding and predicting missinginformation or future trendsSituation awareness1048715 Business Intelligence is about filtering out irrelevant information and setting theremaining information in the context of the business and its environment The userneeds the key items of information relevant to his or her needs and summaries thatare syntheses of all the relevant data (market forces government policy etc)Situation awareness is the grasp of the context in which to understand and makedecisions Algorithms for situation assessment provide such synthesesautomaticallyRisk assessment

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Business Intelligence is about discovering what plausible actions might be taken ordecisions made at different times It is about helping you weigh up the current andfuture risk cost or benefit of taking one action over another or making one decisionversus another It is about inferring and summarizing your best options or choicesDecision support1048715 Business Intelligence is about using information wisely It aims to provide warningyou of important events such as takeovers market changes and poor staffperformance so that you can take preventative steps It seeks to help you analyzeand make better business decisions to improve sales or customer satisfaction orstaff morale It presents the information you need when you need it

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Data Warehouse Implementation Approach Options- Enterprise Data Warehouse

- Dependent Data Mart

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

- Independent Data Mart

- Federated Warehouse

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

The Structure of the Data Warehouse

Metadata1048715 Meta data is descriptive information about data elements or data types such as filesreports workflow processes and so forth1048715 It is typically used for technical activities such as database design and applicationdevelopment But in a data warehousing environment it also becomes veryimportant to end users1048715 It is particularly important for those who plan to access the data directly and developtheir own information applications1048715 They need to understand what data is available for them to access exactly what thatdata represents how current it is and so on1048715 As a data warehouse is built there is a requirement to capture both the data and the

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

meta data Meta data is found in data dictionaries database catalogs programs andcopy libraries and is typically used only by professional programmers1048715 However with data warehousing there is now a requirement to transform the metadata definitions into business terms for end users and provide a mechanism to makeit easy for end users to search for and use this meta data1048715 Meta data guides the extraction cleaning and loading processes as well as makesquery tools and report writers function smoothly

Meta data is used as1048715 a directory to help the DSS analyst locate the contents of the data warehouse1048715 a guide to the mapping of data as the data is transformed from the operationalenvironment to the data warehouse environment1048715 a guide to the algorithms used for summarization between the current detaileddata and the lightly summarized data and the lightly summarized data and thehighly summarized data etc

Data Warehouse Implementation ConsiderationsMethodology1048715 Ensures a successful data warehouse1048715 Encourages incremental development1048715 Provides a staged approach to an enterprise-wide warehouse1048715 Safe1048715 Manageable

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Proven1048715 Recommended Design and Modelingbull Warehouses differ from operational structuresbull Analytical requirementsbull Subject orientationbull Data must map to subject oriented informationbull Identify business subjectsbull Define relationships between subjectsbull Name the attributes of each subjectbull Modeling is iterativebull Modeling tools are available

ETL (Extract Transform and Load)

Purchase specialist tools or develop programsbull Extract - select data using different methodsbull Transform - validate clean integrate and time stamp databull Load - move data into the warehouseData Management1048715 Efficient database server and management tools for all aspects of data management1048715 Imperatives1048715 Productive1048715 Flexible1048715 Robust1048715 Scalable1048715 Efficient1048715 Hardware operating system and network management

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Data Access and Analysis

OLAP - Online Analytical Processingbull On-line Analytical Processing (OLAP) is a software technology that enables analysts managers and executives (sometimes called knowledge workers) to access data usingan easy and efficient query analysis toolbull OLAP complements data warehouses

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Storage ModeMOLAP (Multidimensional OLAP)

ROLAP (Relational OLAP)1048715 Analysis against relational data1048715 Presentation of data in multiple dimensions1048715 Less functionality1048715 Greater data type choice

HOLAP (Hybrid OLAP)1048715 The HOLAP storage mode combines attributes of both MOLAP and ROLAP1048715 Like MOLAP HOLAP causes the aggregations of the partition to be stored in amultidimensional structure1048715 HOLAP does not cause a copy of the source data to be stored For queries thataccess only summary data contained in the aggregations of a partition HOLAP is

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

the equivalent of MOLAP Queries that access source data such as a drilldown to anatomic cube cell for which there is no aggregation data must retrieve data from therelational database and will not be as fast as if the source data were stored in theMOLAP structureWarehouse Data1048715 Numerical measures of the business1048715 Accessed by dimensions1048715 Point-in-time data snapshots1048715 Element of time and dates1048715 Multipart primary key1048715 Indexed primary keys1048715 Non-indexed columns1048715 Many fact tables1048715 Avoid reorganizing factsFact Data Tables1048715 Tables can be large1048715 Data is introduced according to refresh cycles1048715 Data is date stamped1048715 Data allows navigation through history

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Database Keys

Database Keys and Indexes1048715 Primary keys on fact and dimension table columns1048715 Foreign keys on fact table columns1048715 Indexed for speed1048715 Primary keys may be maintained in a

1048715 Composite index1048715 Single column index

1048715 Indexes may be ignored1048715 Generalized keys (or surrogate keys) may be employedGranularity1048715 Affect on warehouse1048715 Size of the warehouse database1048715 Degree of analysis1048715 Flexibility1048715 Level of detail of the data

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Individual transactions1048715 Daily snapshots1048715 Monthly snapshots1048715 Yearly snapshots1048715 Any other time periodFact Table Attributes

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Dimension Data1048715 Dimension data qualifies and drives user query constraints1048715 Design is imperative1048715 Dimension data is linked to fact data by keys

Dimension Data1048715 Data must be of good quality1048715 Data is often expanded for the warehouse1048715 Dimension data is changed not refreshedIt is not usual to completely overwrite the dimension table with a new snapshot of data The change is normally managed in a selective way

Dimension Data Tables1048715 Textual data1048715 Smaller volumes1048715 Contain highly denormalized data

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Quality data is important

Dimension Data Tables

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Normalization1048715 Normalized data contains no1048715 Redundancy1048715 Repeating data1048715 Denormalized data often1048715 Improves efficiency in OLAP systems1048715 Exists in data warehouse databases1048715 Comprises derived or summary dataReference Data and Tables1048715 Supports management of dimension data1048715 Reduces warehouse volume1048715 Provides lookup for encoded data

Summary Data1048715 Provide fast access to precomputed data1048715 Reduce use of1048715 IO1048715 CPU

1048715 Memory1048715 Distill from1048715 Lightly summarized data1048715 Highly summarized data

Summary Data1048715 Average1048715 Maximum

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

1048715 Total1048715 Percentage

Summary Data Tables1048715 Important design consideration1048715 Based on facts calculated by dimension data1048715 Usually exist in summary fact tables1048715 May be many hundreds1048715 Some tools are not summary table awareMetadata1048715 Vital to the warehouse1048715 Data about data1048715 Used by everyone bull ETL metadata - physical design sources mapping rulesbull User metadata - navigation aid business information rulesbull Operational metadata - scheduling analysis

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

Data Warehouse Models- Warehouse Model - Star

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

- Warehouse Model - Snowflake

1048715 Direct use by some tools1048715 More flexible and suited to requirements1048715 Provides for speedier data loading1048715 May degrade performance1048715 More complex metadata

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included

- Warehouse Model - Constellation

Modeling Dimensions1048715 Model according to data content1048715 Model with aggregation needs in mind1048715 Model to satisfy drilling requirements1048715 May be fully denormalized - star1048715 May be fully normalized - snowflake1048715 Construct hierarchies within query tool limits1048715 Categorical dimensions may be included