1. Business AnalyticsBusiness Process and AnalyticsKAIST 2012. 12. 13Copyright 2012. Dr. June Sung Park. All rights reserved.

2. Big DataWhat is the most important partof the term big data? bigdataboth neither2 3. Big DataWhat is the most important partof the term big data?big data bothneitherWhat organizations do with big data is what is most important. Theanalysis your organization does against big data combined with theactions that are taken to improve your business are what matters.Analytics only produces business value if it is incorporated into businessprocesses, enabling business managers and users to act upon thefindings to improve organizational performance. Bill Franks, Taming the Big Data Tidal Wave, Wiley, 2012. 3 4. Enterprise Analytics CapabilityUsing analytics in mainstream business activities is one ofthe effective habits of a successful organization.4 5. Case Study: Amcor Company Global packing company in Australia with 20+K employees and $7B revenue in 2008. Challenge CEO initiated Vale Plus approach requiring measurement of pocket margin of 1M products down to the invoice level. 5 6. Case Study: AmcorProject Phase I: Built a data warehouse over a yearconsolidating data from 32 apps. Biggest challenge was standardizingand cleansing data. Phase II: Built analytics and visualization overa month using an easy-to-use tool. Business people with knowledge ofprocesses helped fit the BI app intoAmcors processes. Rolled out the BI app in 4 stages,conducting usability tests, andtraining users by their businessmanagers. 6 7. Case Study: AmcorResult Lessons Learned 500 users adopted it for daily useLink BI projects with strategic initiatives.in 3 months.Align BI output with corporate KPIs. Incentives introduced that areImplement BI within business processes.based on pocket margins.Have business people, not IT people, Corporate gross margindetermine BI use cases, i.e., where andimproved. how they would use the BI app in processexecution.Take enough time to consolidate silodata into a single version of standardizedand cleansed data.Pick a tool easy for rapid deployment andeasy for users.Have business managers train users. 7 8. Pattern-Based Business Strategy TechnologyMarketYourCompany SupplierCompetitionEnterprises should listen to signals and understand when the signalsare patterns that require adaptation.Listening requires enterprises to combine traditional data sources withnew sources of data.Gartner, Pattern-based strategy: compete in the new economy usingGartners business pattern framework, Sept. 14, 2005.8 9. Pattern-Based Business StrategyCollective Defined Creative Case Management Business ProcessSocial Media Database / Data Mart / WarehouseEnterprise Mobility Service-Oriented Architecture Big Data Analytics Process Orchestration Complex Event Processing Event-Driven Architecture Anticipated Exceptions Anticipated ExceptionsEnterprises should focus their investments on a balanced diversity ofbusiness activities in the defined, creative, collective and exceptionscategories that enable them to innovate and respond to change of patterns. 9 10. Case Study: Investments in Big Data Analytics TXU Energy installed smart electric meters in customer homes and read the meter every 15 minutes. Based on an analysis of the metering data, it applies dynamic pricing to shape demand curve during peak hours. This eliminates the need for adding power generating capacity, saving millions of dollars for the company and saving customer expenditures as well. T-Mobile USA has integrated data across multiple IT systems to combine customer transaction and interactions data in order to better predict customer defections. By leveraging social media data along with transaction data from CRM and billing systems, T-Mobile USA has been able to cut customer defections in half in a single quarter.US Xpress collects about a thousand data elements ranging from fuel usage to tire condition to truck engine operations to GPS information, and uses this data for optimal fleet management and to drive productivity saving millions of dollars in operating costs. 10 11. Pace-Layered IT StrategyCase Management Explorative Apps Social MediaEnterprise MobilityBig Data AnalyticsExploitative Apps Complex Event ProcessingEvent-Driven ArchitectureStable Digital Foundation Business ProcessDatabase / Data Mart / WarehouseService-Oriented ArchitectureProcess OrchestrationApplications move across layers as they mature, or as the businessprocess shifts from experimental to well-established to industry standard.You, however, cannot innovate on an unstable foundation.Many apps for both disruptive and sustaining innovations should bebased on processes and data in the stable foundationGartner, Accelerating innovation by adopting a pace-layered application strategy, Jan. 9. 2012. 11 12. Evolution of Enterprise ITMatured enterprise architecture is today based on standardizedand integrated processes and data, and service-oriented Mobile Cloudarchitecture of apps. Computing(2010-2015) Technical Debt Payoff (2005-2010) E-BusinessProcess-Orchestrated(1995-2005)Mobile + Social + Cloud Cloud ServicesClient/Server+ Big Data Computing Process Management(1990-1995)IT Dark AgeIT ModernizationSOA-BasedOnline (1980-90) Process IntegrationComputingStandardization EA-Based(1970-80)BatchComputing IT (1950-1970) ReengineeringProcessStable Digital Foundation J. W. Ross, P. Weill and D. C. Robertson, Enterprise Architecture as Strategy, HBS Press, 2006.12 13. Stable Digital Foundation Business process management (BPM) is an ideal technology for agile development of explorative, exploitative and core apps for an enterprise. SOA embodies the middle-out architecture where business processes can be reengineered in flight to quickly implement new business use cases reusing core business services. Business service repository and data federation layers virtualize and synchronize physical apps and data to provide an integrated and standardized foundation.Composite AppsBusiness Process Composition Business Service RepositoryMetadata-Based Data Federation Physical AppsPhysical Data SourcesGartner, EIM reference architecture: an essential building block forenterprise information management, Sept. 14, 2005. 13 14. Enterprise ArchitectureBusiness Architecture BPM ACMBusiness-IT AlignmentApplication ArchitectureSOA EDAData Architecture MDM Big DataTechnical ArchitectureTRM Virtualization EA is a strategy planning process ensuring business-IT alignment across the enterprise using the architectural approach. Matured EA employs BPM, SOA and MDM disciplines to enable quick alignment between business use cases and app delivery by reusing common master data and core services. 14 15. Enterprise Architecture Strategy PlanDemand Plan As IsTo Be BA AAIT Asset DA Investment MgmtPlan TA TransformationProject Portfolio Mgmt 15 16. Business Process Management BPMN 2.0 DesignGraphical modeling,process simulation,business rules BPEL4WS, BPEL4P ImplementCode generation Execute BPMSAutomation, workflow andintegrationMonitor Business activity monitoring, automated process discovery and dashboardsOptimizeAnalyze and dynamically adjustbusiness processes and rules16 17. Business Process Modeling EnterpriseContent Mgmt Data AnalyticsData modeling is designing the intended use of data.Process and data modeling cannot be done separately. 17 18. Business Process ReengineeringAdaptiveCase ManagementSocial Collaboration Process innovation is often enabled by redesigning the flow of information. 18 19. Adaptive, Intelligent and Social BPMAnalytics, social network and adaptive case management are integrated into BPMfor performance monitoring and reporting, forecasting, scenario modeling,complex decisions, planning, real-time situation recognition, immediate nextaction recommendation, etc.Enterprises need business process and performance management maturity thatenables cross-functional accountability and top-down/bottom-up informationflows. EnterpriseContent Mgmt Data Analytics Adaptive CaseSocialManagement CollaborationForrester, Forrester wave: dynamic case management, Jan. 31, 2011.19 20. Adaptive, Intelligent and Social BPMIntegration of analytics into operational processeswhich contrasts with pastapproaches that separated analytical work from transactional workempowers the workforce to make better and faster contextualized decisionsin order to guide work toward optimal outcome, and its impact is immediatelyapparent to business people because it changes the way they do their jobs.http://bps.opentext.com/resources/ot_bps_OT-Process360_ds.pdf20 21. BPM Maturity Model Enterprises usually cannot skip maturity levels. Enterprises should develop a long-term roadmap to improve their maturity level, based on the current state assessment and the readiness check for the next immediate actions. SOAEA BPR iBPMS BSCGartner, ITScore overview for business process management, Sept. 17, 2010. 21 22. Advanced BPM InitiativesTomorrows business operations requireintegration of real-time intelligence.Process is the unifying construct for intelligentoperations.Integration of BPM and automated analytics intoSOA-based iBPM is an important businessevolution underway.Gartner, Business process management key initiative overview, July 22, 2011. 22 23. iBPMS Talend provides open source solutions for data integration, data profiling, data cleansing, master data management, enterprise service bus, Hadoop connection, cloud enablement, and BPM. Using Talend solutions, you can load data from multiple sources into a master data hub as a SoR, apply the data quality tool to resolve data conflicts, and provide clean data services for automated decisions in business processes or for business workers whose workflow is orchestrated by BPM.23 24. iBPMSiBPMS has 10 corecomponents:Orchestration enginefor processes andcasesModel-drivencompositionHuman-drivenworkflowContent-drivenworkflowConnectivity ofprocess to resourcesActive analyticsOn-demand analyticsBusiness rulemanagementProcess repositoryBPMS administration 24 25. Service-Oriented Enterprise ArchitecturePortal Business Process Business Service SaaSComponent MetadataService Data Mart / Warehouse Database Big Data 25 26. SOA Implementation using BPM Suite BPEL ProcessProcess Redesign using BPMN Process KPI Definition Process Simulation Implementation ServiceBPM UI and Monitoring ServiceIntegration Test SpecificationImplementation Realizationand Execution 26 27. Linking BPM to Analytics based on SOA: SAP NetweaverBPM-specific BI content inInfoCube (star schema)OLAP dataQuery on InfoCube Result in WSDLDashboard renderingdata from BPM27 28. Enterprise Information ManagementInformationgovernance andmetadatamanagement iscritical to anyinitiative thatuses data todriveimprovements tobusinessoutcome.28 29. Enterprise Information Management29 30. Enterprise Information Management Initiative Through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage. 1Explore fundamental technology trends, such as big data, mobile, socialmedia, cloud computing, and how they reinforce each other to offeropportunities and risks. 2Plan based on business strategy and enterprise architecture. 3Model business requirements and detail specification for solution delivery. 4Choose technologies and vendor/service providers. 5Implement, test and release the solution iteratively, seeking user feedback. 6Operate the solution, measure performance, revise the solution and refinegovernance processes.Gartner, Information innovation: innovation key Initiative overview, Apr. 27, 2012.30 31. Analytics FrameworkAnalytic apps can work with any kind of data, including transactions, events,unstructured contents, website data, social networks, and Internet of things(machines, sensors). IncreaseAnalytics, however, should resolve management challenges first.analytical skillsEmbed analytics into theEstablish corporate of centralized business process and workflow.performance metrics.analytic team as well as self- service analysts within business Attract units.corporate execs to participate. Ensure dataquality andFind use cases consistency.and justifybusiness cases.Build requirementCreate engineering organizationcompetency. culture of valuing fact-Consumerize Balance between standardization and diversification,based decisions. through mobile custom-design and packaged apps, on-premise and delivery. cloud, SQL and NoSQL, storage and in-memoryGartner, Analytics key Initiative overview, July 22, 2011. 31 32. Analytics Maturity Model Enterprises usually cannot skip maturity levels. Enterprises should develop a long-term roadmap to improve their maturity level, based on the current state assessment and the readiness check for the next immediate actions.Gartner, ITScore overview for business intelligence and performance management, Sept. 17, 2010.32 33. Analytics Roadmap PlanningEnterprises should assess the current level of maturity using a analyticsframework, find areas of weakness and opportunities for improvement,set up a long-term roadmap to raise the maturity level, follow the EAprocess to determine and execute short-term improvement initiatives,and put in a continuous improvement program. Data Consistency and QualityCulture ofAnalytic Fact-Based DecisionCompetenciesRequirementProcessEngineering and MetricsMethodology Exec Commitment and Governance33 34. Analytics LifecycleAcquire dataOrganize data ETL orELT Data platform Data source(DB, DW,Hadoop) Set requirements Select and buildand hypothesesmodelsAnalyze Take BPM Analytics dataaction for insightEmbed into Extract rules operationBRMMake decision 34 35. Analytics Methodology: IBM and Capgemini 35 36. Analytics Methodology: IBM 36 37. Analytics Requirement Metamodel Big data needs big process. (Forrester Research) Big data without a process context and a compelling use case for a specific user class is like a Maserati without an engine. Big data with proven values will become structured.Process ModelUse Case Model UX ModelBusiness Process ActorUse CasePersonaRule ActorI/O Info ProcessEventCommunication ActivityAssociationUse CaseUser Task Information Model Service Data Use CaseUser TaskScenarioScenario Dictionary Analytics User ConceptGlossaryData Model Map37 38. Analytics Requirement Engineering for SOA EnterpriseBusinessArchitectureStrategy ConceptualProcess ModelUXConceptual ModelData Model BusinessUse Case Conceptual Reqts ModelService ModelExecutableProcess ModelSoftwareLogical Data ReqtsSchema UI Use Case Design Scenario Analytics Test Service Case Specification38 39. Analytics Requirement Engineering for SOADesignModel Portal UXUIBusinessCaseTestProcessScenarioBusinessCaseUse ServiceSaaS ComponentProcessProcess Model ExecMetadata Service Data Mart / ServiceService WarehouseModelSpecDatabaseSchemaBig DataModel DataData Service-Oriented Architecture 39 40. Analytics Requirement Engineering for SOA: IBM Service Service ProcessUse Case Model Process Model SpecificationImplementation OrchestrationIndustryReference ModelData ModelIBM, Building service-oriented solutions with IBM industry models andRational software development platform, 2007. 40 41. Analytics Requirement Engineering for SOA: Capgemini 41 42. Case Study: PayPalCompanyGlobal e-commerce business allowing payments and money transfers to bemade through the Internet.Role of Global Business Analytics Team Managing Down: Ensure connection between the analysis they do and theactions the company takes. Work closely together with business peoplefor right questions and right interpretation of findings. Managing Up: Establish themselves as thought partners, not dataproviders, to the executive, and translate analytical insights into actionablerecommendations.VeronikaBelokhvostova, HeadAnalytics Team Membersof Global BusinessBusiness analysts with a mix of technical and business skills. Most havingAnalytics at PayPalMBAs in addition to data analysis skills.Project ExamplesAnalysis of customer behaviors and interactions for improving products andmarketing, analysis of the impact of website redesign, analysis of the effectof promotional pricing, diagnosis of of revenue leakages, analysis of theimpact of risk management policies on customers, etc. Renee Ferguson, Mining data at PayPal to guide business strategy (Interview with Veronika Belokhvostova), MIT Sloan Management Review, Sept. 2012.42 43. Process-Driven Big Data Analytics Initiative Big data analytics requires a data-savvy business strategy to achievecompetitive advantage. Keep the process transparent; it iskey to successful big data projects. Educate process owners aboutpotential big data opportunitiesnow readily available through start-small, cost-effective analytics toolsand techniques. The value delivered from aninvestment in big data analyticsmust be visible and measureable. 43 44. Process-Driven Big Data Analytics Initiative Use low-cost, open-source tools inearly pilots to demonstrate thefeasibility of big data projects. Explore the increasing number ofpublic datasets now available throughopen APIs. Produce a resource plan that identifiesbig data skill gaps. Look for business-savvy analysts (especially datascientists) and analytics-savvybusiness leaders who can worktogether to find what business shoulddo based on analytic results and thendo it. Assess resource needs for informationinfrastructure and identify technicalgaps when supporting big datasolutions. 44 45. Data Scientist Business Use CasesAnalytics AppsAnalytics Common ServicesRT-OLAP Analytic Algorithms Visualizatione.g. BigQuerye.g. Greenplum e.g. Pentaho In-Memory DataData ModelsETL e.g. GridGain e.g. NoSQL, RDB e.g. KettleBasic Data Transformation e.g. Map Reduce, Pig, Hive, Sqoop, LuceneFile System NoSQL DB e.g. HDFSe.g. Hbase (In-Memory) Stream Processinge.g. Flume, AvroDistributed Agents Thomas Davenport and D. Patil, Data scientist: the sexiest job of the 21st century, Harvard Business Review Oct. 2012.45 46. Case Study: SearsCompanyAmerican chain of department storesChallenge Decided to generate greater value fromthe huge amounts of customer, productand promotion data collected from itsstores. Took 8 weeks, due to highly fragmenteddatabases and data warehouses, togenerate personalized promotions, atwhich point many of them were nolonger optimal.Andrew McAfee and Erik Brynjolfsson, Big data: the managementrevolution, Harvard Business Review, Oct. 2012.46 47. Case Study: SearsSolution Set up a Hadoop cluster in 2010,and used it to store incoming datafrom its stores and to hold datafrom existing data warehouses. Conducted analyses directly on thecluster, with the processing timereduced from 8 to 1 week, and stilldropping. Got help from Cloudera initially,but over time internal IT andanalysts became comfortable withthe new tools and methods.47 48. Q&A