midas whitepaper

8/9/2019 MIDAS Whitepaper

1/15Copyright 2009. Bodhtree Consulting Limited. All Rights Reserved.

Leading the Challenge in Data Quality

Multi Industry Data Anomaly Solution (MIDAS)

Bodhtree Consulting Limited

8-2-351/N/1 Road No 2,

Banjara Hills, Hyderabad 500034

AP India

Tel: +91-40-66547000

Fax: +91-40-66547029

THIS DOCUMENT AND INFORMATION HEREIN ARE THE PROPERTY OF

AND ALL UNAUTHORISED USE AND REPRODUCTION ARE PROHIBITED.


2/15

Table of Contents

1 The Business need or quality data

2 Data Quality Management

3 Multi Industry Data Anomaly Solution (MIDAS)

4 Bodhtrees Value Proposition

3.1 MIDAS Technology

3.2 MIDAS Oerings

3.3 MIDAS Expertise

3.4 Healthcare

3.5 Pharmaceutical

3.6 Social Networking Analysis

3.7 Publishing

3.8 Midas - Healthcare Implementations

3.9 Midas Pharmaceutical Implementation

3.10 MIDAS CRM Integration

3.11 MIDAS - Advanced Analytical Engine

3.11.1 MIDAS - Marketing Campaigns

3.11.2 MIDAS - Sales

3.11.3 MIDAS - Service

3.11.4 MIDAS - Web Analytics

3.11.5 MIDAS - Health Care Cost per Adjusted Patient Day

3.11.6 MIDAS - Health Care Operating Margin

3.11.7 MIDAS - MIDAS - Proft and Loss Account

5 Select Partners & Customers

6 Reerences

7 Contact

www.bodhtree.com

2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

[email protected]


3/15www.bodhtree.com

2009 BodhtreeTelephone: 91.40.6654.7000Fax: [email protected]

1.The Business Need For Quality Data

Early in 2007, during the Gartner Predictions or 2007:Customer Relationship Management conerence held on 23 Jan

2007, Gartner predicted that Poor customer service will undercut all IT eorts. It was recognised that re-architecting

the major application suite providers platorms would delay the adoption o next generation o customer service

contact centre products until the rst hal o 2008. The recommendation, in the interim, was to ocus on other

high-value tasks, such as data cleansing, data integration and adding analytics or realtime oers and lead capture.

Ted Friedman, vice president o data management and integration at Gartner, and author o the Gartner report Magic

Quadrant or Data Quality Tools, 2007, says that Companies have discovered that data quality has a signicant impacton their most strategic business initiatives. Poor data quality severely inhibits many strategic business initiatives such

as customer relationship management (CRM), business intelligence (BI) or any eort requiring signicant integration o

data. Here are some current statistics on data quality.

According to The Data Warehousing Institute, U.S. businesses suer losses, problems or costs o more than $600 billion

per year due to poor quality data, and typical business between 10% & 25% o revenues. Source

Aug 2008 newsletter o ECCMA

By 2012, dirty data will cause 50% o insurers to have compromised decision-making assumptions, despite the

deployment o enhanced BI and analytic tools. Source - Gartner Inc. research report o Feb 2008

A European Gartner BI survey o more than 600 BI users ound that more than 35 per cent identied data quality as a

top-three BI problem acing their organization in the next 12-18 months, making it the second biggest challenge

overall. Source - Gartner report o 22 January 2008 titled Organizations Must Establish Data Stewardship Roles to

Improve Data Quality

Although 92% o companies surveyed believe having an integrated view o customer data is either critical

or very important, only 2% have actually managed to achieve this. Source - Forrester Research report

Research by Marketing Direct magazine indicates that direct marketers waste 195m in postage costs by sending

incorrectly addressed international mail

A survey by QAS ound that 28 percent o the direct marketers admitted that their company rarely cleans its data or in

some cases, not at all

88 M per year is spent mailing the deceased(!), 100m per year mailing people who have moved house

Up to 67% o UK B2B mailings contain errors

37% o companies still do not have a data quality strategy in place

88% o all data integration projects ail because o poor quality data Source http://www.dqglobal.com/,

http://www.datafux.com

Data quality is a business problem, not an IT problem. Technology is not going to solve the problem. The business side

needs to be involved in being accountable or the data quality and in maintaining the data. Business users know what

the data should look like; IT knows where it is and how to access it. Without cooperation and support o both the

business sta and IT, the data tends not to meet the needs o the company.

Another mistake is to believe that data quality is a once done complete approach. Data quality has to be monitored or

consistency and value. Dierent stakeholders have dierent data needs. The importance o data also varies over time

what is useul today may not be relevant down the line.

Who owns the data?



2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

[email protected]

2. Data Quality Management

Data quality management initiatives ocus on the process o ensuring that an organizations data assets are o sucient

quality to meet its needs. It is well known that what you can measure, you manage. Data quality is no dierent, so frst

you must defne what good data is, then measure, analyse, improve and control it so that you know where you are on

the data asset improvement journey at any time.

Data has no value unless it can be used to make sound corporate decisions. Making decisions based upon bad data

leads to bad decisions being made with a high degree o certainty! Invalid inormation, so-called dirty data, increas-

ingly populates databases and operational history fles. Reliance on unrecognized, oten erroneous, data points to makebusiness decisions compromises the integrity o those decisions. Data quality oten defnes the success or ailure o CRM

implementations. Gartner recommends that companies engage in data-cleansing projects in conjunction with business

intelligence adoption (reer Gartner: Insurers Must Invest in Tech to Meet Coming Trends by Pat Speer February 6, 2008).

Recognising the importance o data quality and the need to evolve standards or codifcation in the entire supply chain

system, the International Organisation or Standardisation, ISO, has come out with two standards ISO 22745 that that

covers the tools or encoding data and ISO 8000 or inormation quality in terms o encoding, completeness, origination

and accuracy. Through a memorandum o agreement signed with Electronic Commerce Code Management

Association (ECCMA) in October 2004, the NATO Allied Committee 135 (AC/135) has promoted the NATO Codifcation

System as an international standard. The ECCMA Open Technical Dictionary (eOTD) is an industrial version o the Military

NATO Codifcation System (NCS). Along with the associated XML interchange ormats, a vendor can build master data

that meets ISO 8000 data quality standards. These codes can be used in the entire data lie cycle management, rom

design to disposal, and allows or seamless date exchange between producers, distributors, customers and service

personnel, as shown in the diagram below. According to Friedman, the more powerul strategic approach to data

quality requires a more complex perspective: profling, standardization, matching, and enrichment. And while customer

data has always been and continues to be the primary ocus, the Gartner report notes that organizations are

increasingly looking to deploy data quality tools in other subject areas--in particular, product data and fnancial data.

Data standards problems can occur in many areas, ranging rom invalid mailing addresses to improperly ormatted data,

such as a manuacturer part number that omits either a crucial prefx or sux. As shown in the diagram below, data

anomalies can arise rom various reasons.

Source Defence Logistics Information Service, Battle Creek, MI



2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

[email protected]

Data Anomalies

Incorrect

Inaccurate

Incomplete

Duplicate

Inconsistent

Missing

Completeness does the organization have data assets that are incomplete or missing? Eg do all customershave addresses?

Accuracy Is the organizations data assets suciently accurate to meet internal (businessprocesses, decision making) and /or external (regulatory, third parties) requirements?

Integrity Are the organizations data assets consistent across the enterprise? or ex, does the list o suppliers

in a companys ERP system match those in the nance application? Do the relationships between diferent data assets

make sense? Are duplicates removed rom the system?

The above anomalies can be grouped into three main areas o interest with respect to the quality o the data:

Methodology

The general methodology in moving to a data quality standard across the enterprise involves the ollowing stages:

1. Data Proling - process o examining the data available in an existing data source (e.g. a database or a le) andcollecting statistics and inormation about that data and includes column proling, dependency proling and

redundancy proling. In data quality proling, you identiy what your deects are, and how your data compares against

your business rules, says Frank Dravis, vice president o inormation quality at FirstLogic.

2. Metadata Analysis understand the data, extract and organize them rom any source within the organization

3. Outlier Detection detect data values requiring urther investigation

4. Data Validation dene data types and constraints on data



2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

[email protected]

5. Pattern Analysis analyse or correct data ormats

6. Relationship Discovery discover relationships eg primary - oreign key constraints

7. Statistical Analysis perorm statistical analysis, min-max values

8. Business Rule Validation perorm domain checking, range checking, look up validations etc

9. Data Quality / Enrichment cleanse, standardize and categorize

10. Data Integration integrate data rom disparate sources within an enterprise. This may involve data

transormations rom various sources into the target application

11. Data Monitoring review data periodically to take corrective action

Master Data Management or Make-Do-Mend?

The fnal goal is to move to a Master Data Management System across an enterprise. Building an MDM is a

multidisciplinary project. An MDM strategy would beneft large organisations with diverse business unctions,

e.g.,fnance, sales, R&D, etc. which oten extend over several countries, or companies ormed by acquisition or merger.

These diverse systems usually need to share important or strategic data related to business intelligence, products,

customers and suppliers. MDM integrates the inormation rom existing data sources, consolidates them into a master

data fle, eeds the inormation back to the sources and thus allows consistent and accurate data tobe used across the

enterprise. This can include both Customer Master Data Management and Product Master Data Management.

Customer data integration (CDI), or the process o consolidating and managing customer inormation rom all

available sources, i properly carried out, ensures that all relevant departments in the organisation have access tothe

most current and complete view o customer inormation.

The challenge is to create a common system or all users to access the inormation according to need, as well as

maintain accurate master data. This means that everyone needs to own the problem o data quality, seeing it as a

corporate asset.

3. Multi Industry Data Anomaly Solution (MIDAS)

Bodhtree Consulting Limited is ISO 9001:2008 certifed and is headquartered in Hyderabad, India with presence

in USA, Thailand and Malaysia. Bodhtree was ounded in 1999 by entrepreneurs rom Silicon Valley and is managed by a

proessional management and reputed board o directors.

Bodhtree has been providing Data management Services, Spend Management Services and Business Intelligence

Solutions to Fortune 250 and Forbes clients in Healthcare, Publishing, Media, Pharma, Lie Sciences, Financial,

Entertainment, Retail and Distribution, with a customer base o over 300 customers.

3.1 MIDAS Technology

MIDAS is open standard based J2EE application which adheres to best industry practices in orm o vertical

specifc pre-canned data Hygiene steps, data connectors and reports. More over MIDAS is SOA & JSR 168 compliant

architecture and comes with Role based access control (RBAC) mechanism. It has inbuilt data connectors or industry

leading OLTP / ERP / DSS systems:

Quickbooks

EDI Connectors

JMS & XML

CSV and Excel Files

Transmission o data is handled through secure FTP channel

with data security compliance to BS-7799 equivalent standards.

MIDAS is provided with 24/7 support rom global delivery

centers (GDC).

SAP

Oracle EBS

Salesorce

SugarCRM

Siebel onDemand



2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

[email protected]

3.2 MIDAS Oferings

MIDAS addresses data hygiene issues across dierent verticals, using proprietary tools and processes which are exible,

scalable and robust.

MIDAS ofers convenient options:

SAAS, where in customer data has been transmitted to

Bodhtree servers using secure FTP layer. Bodhtree perorms

data hygiene operations and report back results and enriched

data through web based portal. On Premise, where in Bodhtree installs MIDAS at customer

premises using data hygiene jump start templates. Jump start

templates are set o tools and processes that Bodhtree has

built over years o experience in handling critical customer

needs. These templates enable aster deployment o solution

at customer end.

Turn-key Solutions, where Bodhtree proven processes would

be applied at customer location using customer specifc tools.

MIDAS process is tool agnostic and can be applied on

customer data using any existing toolset that customer owns.

Bodhtree team has expertise with ollowing data managementtools.

Inormatica

BO Data Integrator

Oracle Warehouse Builder XFusion (SAP Certifed Product)

Pentaho Data Integrator (Open Source Product)

JBoss Metamatrix

Pervasive

3.3 MIDAS Expertise

MIDAS has proven implementations in ollowing industries.

Healthcare

Financial Services

Pharmaceutical

Retail & Online Services Media and Entertainment

CRM

EDI Transactions / Claims Processing

SCM

Product Data Management

A ew samples o Bodhtree oerings in the

data management across various verticals are

given below.

3.4 Healthcare

Bodhtree provides data cleansing services, contract digitization services, price parity analysis and maintenance o

portals or various hospitals and distributors in the healthcare supply chain market. The Item master and vendor

master are enriched, standardized and categorized and reports o cleansed items are published on the data

cleansing portal.

Vendor ID Vendor Item Item Description Item Master Price

59320 DS-1510

DRSG SURG COMBINE STRL 18.45

SURGIPAD(tm) 8INX7.5IN 18.45

59320 DS1510 Dressing Surgical Combine 25.60

Same Same Differential PricingA-M SYSTEMS INC

AM SYSTEMS

ABBOTT DIAGNOSTICS DIVISION

ABBOTT DIAGNOSTICS

ABBOTT

A-M SYSTEMS INC.

ABBOTT DIAGNOSTIC DIVISION



2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: [email protected]

In addition, customized Purchasing Trend Reports are produced, some o which are mentioned below.

3.5 Pharmaceutical

In the Pharmaceutical industry providing single view o the molecule across disparate systems is crucial. Typically

molecule would pass dierent stages such as

. Inception . Pending File Approval

. Sales . Under Development

Top Vendor Spend Report

Identiy product and unit purchases by vendor or a single

hospital and across the health system.

Top Manuacturer & Manuacturer Divisional Spend Report

Identiy product and unit purchases by manuacturer or a

single hospital and across the health system.

Top Item Spend ReportIdentiy product and unit purchases by item or a single

hospital and across the health system.

Top Spend by Category Report

Identiy product and unit purchases by United Nations

Standard Products & Services Codes (UNSPSC) categories

or a single hospital and across the health system.

Top Spend by Department (Location) Report

Identiy product and unit purchases by department or a

single hospital and across the health system. This requires

department codes and cross-reerence inormation becontained in the PO History fle.or a single hospital and

across the health system.

As molecule passes these stages it would change its names as new compositions and brand names are added.

A standardization o molecule names is useul or tracking molecule journey in all stages.

3.6 Social Networking Analysis

It is possible to use social networking tools to map and characterize scientifc communities. Research personnel and

scientists publish papers and articles; they also move rom one institution to another. For example a person can be an

author on an article; or two people can be co-authors on an article. In either case, the same author must be identifed

correctly on multiple articles. In short we must answer the question: Is J.Smith on article one the same J.Smith

on article two?

The ability to identiy the same person that has had a name change (e.g. maiden name vs. married name) is neigh

impossible automatically, but as a persons master record is built, the goal is to retain a list o all possible names or a

particular person, in order to successully match uture additional records or the same person.

Let us take an example o three publications rom the author Peter Serozo.

Selective migration o neutralized embryonic stem cells to stem cell actor and media conditioned

by glioma cell lines Peter Serozo#1, Maggie S Schlarman#1, Chris Pierret1, Bernard L Maria2, and Mark D Kirk1

Cancer Cell Int. 2006; 6: 1. Published online 2006 January 25. doi: 10.1186/1475-2867-6-1.

1Division o Biological Sciences, 114 Leevre Hall, University o Missouri, Columbia MO 65211

2Charles P. Darby Childrens Research Institute, Medical University o South Carolina, 135 Rutledge Ave.,

Charleston, SC 29425

#Contributed equally.

Identifcation o the True Product o the Urate Oxidase Reaction Kalju Kahn, Peter Serozo, and Peter A. Tipton*

Am. Chem. Soc., 119 (23), 5435 -5442, 1997. 10.1021/ja970375t S0002-7863(97)00375-2

Copyright 1997 American Chemical Society

Contribution rom the Department o Biochemistry, University o Missouri-Columbia, Columbia, Missouri 65211

Received February 4, 1997

Identifcation and Purifcation o Hydroxyisourate Hydrolase, a Novel Ureide-metabolizing Enzyme*

Annamraju D. Sarma, Peter Serozo, Kalju Kahn, and Peter A. Tipton

J Biol Chem, Vol. 274, Issue 48, 33863-33865, November 26, 1999

From the Department o Biochemistry, University o Missouri, Columbia, Missouri 65211

In the 1st article, Peter Serozo is the author with one set o authors rom Charles P. Darby Childrens Research Institute.

In the 2nd and 3rd articles, Peter is the 2nd author with dierent researchers. The need is to merge all these dierentdata and create a Master Record or Peter Serozo.



2009 Bodhtree

Telephone: 91.40.6654.7000


3.7 Publishing

In the publishing industry and book stores, inormation on a book may be stored is slightly varying ormats.

A standardization o author names and book titiles is useul or tracking sales and inventory.

Vendor 1 Author Names Vendor 2 Author Names Reason for Non Match

Burton (translator), Sir Richard

FrancisSir Richard Burton (translator) The frst name and last name is

reversed and also Francis is

missing in Vendor 2 Author

name

Fu His, Emperor Emperor Fu His First name and last name

reversed

First name and last name

reversed

L. Ron HubbardHubbard, L. Ron

Donatien is missing in Vendor

2 Author name

Marquis De SadeMarquis De Sade, Donatien

First name and last name

reversed

Kenneth RobesonRobeson, Kenneth

Bishor is missing in Vendor 1

Author name

Augustine, Saint, Bishop o

HippoSaint Augustine o Hippo

Comma is missing ater

Daisetz in Vendor 2 Author name

Suzuki, Daisetz TeitaroSuzuki, Daisetz, Teitaro

First name and last name reversedYamamoto TsunetomoTsunetomo, Yamamoto

3.8 MIDAS - Healthcare Implementations

. Partnership with Owens & Minor (Fortune 250 Healthcare Supply Chain Solutions company

with > $7 Billion annual revenue)

. Implementations at some of the largest healthcare providers in US:

University o Kentucky

University o Rochester

Catholic Health East

Staten Island University Hospital

Laayette General Medical Center

Parrish Medical Center

University o Caliornia, LA

Memorial Hermann Health Systems

IOWA Health System

University o Texas - South West West Chester Medical Center

Lennox Hill

New York University

Yale New Haven

Allina Hospitals and Clinics

Stellaris Hospitals

NSLIJ Hospitals

Premier Health Partners

University o Louisville

John Hopkins

Bon Secours

Sparrow Medical Center



2009 Bodhtree

Telephone: 91.40.6654.7000


3.9 MIDAS Pharmaceutical Implementation

Executive Dashboard (BIS): Open standards based comprehensive dashboard solution or C group. Integrated with BI

inrastructure and intranet portal. 3 Phases completed. Oct 02 to Dec 04. Phase 4 started and expected to end by

Mar 09.

Product Portfolio Management: Centralized view o molecule, rom inception, under development, pending approval to

sales. Integrating dierent OLTP systems. Completed in Mar06. Phase II started and expected to end by Mar 09

Financial Data Mart: Financial Data Mart Solution or Formulations SBU, with sales, budgets, manuacturing

inormation. Jul04 to Dec 05.Integrated Demand & Supply Planning System: Implemented Integrated Decision Support System or capture and

analysis o all data required by Demand and Supply Planning Processes. Serves as a platorm or calculation and

reporting o Demand and Supply Planning Metrics. Partnered with Accenture, where Accenture defned the SCM

processes and Bodhtree provided BI technology. 2 Phases completed. Jan 05 to Feb 06.

3.10 MIDAS CRM Integration

- MIDAS integrates with industry leading CRM vendors, Salesorce and Siebel on Demand.

- Data in the CRM applications is accessed using MIDAS secure web services API.

- Using MIDAS users o CRM applications can detect duplicates among contacts, leads and prospects.

Siebel CRM On Demand

3.11 MIDAS - Advanced Analytical Engine

- Nearly 70% o the business metrics that executives in various industries track are common.

- Custom BI projects have < 30% success rate due to - Long Time-to-Value, Shel-ware, Tepid Exec

Support, Lack Implementation Expertise;

- Bodhtree is leveraging prior implementation experiences to build Pre-Built BI solutions & oer them to

customers in SaaS / Jumpstart models, MIDAS in ollowing areas:

>> Enterprise Analytics (Marketing, Sales, Service, Marketing Product)

>> Healthcare Analytics (Operational, Spend, Clinical and Financial)

>> Pharma Analytics

>> Service Analytics (Call Center Analytics)

- Ad Hoc Analysis: Business users can slice and dice around specifed data cube and perorm advanced

analysis o the data. Ad Hoc Analysis allows you to create various charts based on the business requirements and

analyze the data as per the need.

- Ad Hoc Query: Users can create detail reports by perorming GUI based query tool.

- Dashboard: A dashboard is a visual display o the most important inormation needed to achieve one or more

objectives; consolidated and arranged on a single screen so the inormation can be monitored at a glance.

- Report Portolio: Pre-canned domain specifc reports which provides insights into details o data.

- Data Model: Pre-designed data models or analytical purpose.

-Data Mappings: Pre-built data mappings or extract, transormation and loading o data.

300+ pre built metrics and reports

>> Finance Analytics

>> Retail Analytics

>> Media Analytics


11/15

2009 Bodhtree

3.11.1 MIDAS - Marketing Campaigns

3.11.2 MIDAS - Sales


12/15

2009 Bodhtree

3.11.3 MIDAS - Service

3.11.4 MIDAS - Web Analytics


13/15

2009 Bodhtree

3.11.5 MIDAS - Health Care Cost per Adjusted Patient Day

3.11.6 MIDAS - Health Care Operating Margin


14/15

3.11.7 MIDAS - Proft and Loss Account

4. Bodhtrees Value Proposition

Bodhtree oers a truly scalable partnership, rom sotware development services to strategic business relationship.

Outstanding sotware engineering expertise

- Increased eciency through structured process and standards tools

- Real time visibility and accountability contributes value beyond cost eciencies and ensures there are no surprises

- Highly optimized development environment

- Flexible processes, periodic status reporting, deep skills, stable teams

Leading client base and reputation as a long-term partner

- Successul relationships with highly reputed clients

- Open to explore new mutually benefcial business opportunities

- More than accommodative with true partnership spirit

5. Select Partners & Certifcations

6. Select Customers


15/15

2009 Bodhtree

Telephone: 91.40.6654.7000

Fax: 91.40.6654.7029

6. Reerences

1. Fazal S Ahmed

Associate Director

Information Systems

Dr Reddys Laboratories Ltd

7-1-27, Ameerpet,

Hyderabad, AP, India.

500016Mobile:- +91-99890-58868

Ofce:- +91-40-66511953 (Direct)

Email:- [email protected]

2. Carl Natenstedt

Operating Vice President

Commercial Technology & Innovation

Owens & Minor

621 East 6th Street

Austin, TX 78701

Mobile:[email protected]

7. Contact

For Further details please email to [email protected]

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