11

Data Strategy for MDM Implementation

임정혜

한국인포매티카

2008.3.5

2

Agenda

• MDM Overview

• Informatica’s Value Prop for MDM

• Case Studies

3

What is Master Data?

Vendor

• You are Master Data

• You buy from Master Data

• You buy Master Data

• What you do, or create is Transactional Data

ProductCustomer

Campaign Order Invoice ShipmentReceive Payment

Request a proposal

Master DataTransactional Data

4

Sales

Contact ManagementSales/Pipeline Management

CompensationWin/Loss AnalysisSales Information

Call Center AutomationTeleSales Scripting

Email Routing & Management

Online Communications

Finance

Sales/Revenue ForecastingPurchasing

AR/AP systemsFraud IdentificationAudit Fin Review

Marketing

Market Plan DvmntOutbound Message Mgt

Competitive TrackingCustomer TargetingCustomer ProfilingSegment Analysis

CustomerSupport

SystemsFinance HR

The Challenge of Managing Master DataINFA is no different than Customers

Organizations

Customer Service FinanceSales Marketing

• Multiple Lines of Business

• Multiple Applications

• Multiple Sources of Master Data

5

Master Data and Data Quality Rapidly Gaining Importance in Enterprises

“By 2005, Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on data warehouse and CRM initiatives.”

Source: Gartner

Corporations are increasingly focusing attention to master data management and data quality due to increased pressure for corporate transparency, accountability, and performance on a enterprise-wide basis.

50% of enterprises surveyed maintain master data separately in 11 or more source systems50% of enterprises surveyed consider master data management to be a high or top priorityOver 80% of enterprises surveyed plan on centralizing master data management

Source: Tower Group

6

Source: Gartner Dataquest (December 2006)

0

100

200

300

400

500

600

700

800

2003 2004 2005 2006 2007 2008 2009 20100%

20%

40%

60%

80%

100%

120%

140%

160%Revenue

Growth

$102.9m

$43.2m

$274.1m

$189.5m

$386.3m

$485m

$585.3m

$706m

MDM Market

0

200

400

600

800

1,000

1,200

1,400

2005 2006 2007 2008 2009 20100%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Revenue ($M) Growth

Revenue (Millions of Dollars) Grow th

$322m

$214m

$638m

$462m

$845m

$1061m

Customer Hub, 2003-2010Significant growth ahead

Product Hub Market, 2005-2010PIM seems to be an even larger market

7

Generally Accepted MDM Principles

• MDM is an APPLICATION• Even if homegrown

• Composed of:• Data Models• Business Processes• User Interfaces• Role based users• Integration with the rest of the enterprise

8

MDM Application S/W Vendor Landscape

Tools To Build an MDM Application

KalidoOrchestra Networks Stratature Teradata

Product Information Mgmt

Teradata

Sterling Commerce

SAP

Customer Data Integration

Financial

Hyperion

Microstrategy

Microsoft

SiebelIBM/WPC

IBM/WCC

Initiate

Siperian

Tibco

Cognos

Oracle

IBM

SAP

Sunguard

GXS D&B

SAP

9

What Does an MDM Application Look Like?SAP

10

Agenda

• MDM Overview

• Informatica’s Value Prop for MDM

• Case Studies

11

Informatica Positioning and Value Prop for MDM

• We do not provide an MDM Application

• We complement MDM Applications

• We enable homegrown MDM initiatives

• DI and DQ are critical for MDM project success

12

Mainframe

Applications

Databases

XML

Messages

UnstructuredData

Data Model

Enterprise Connectivity

Address Validation

Data Profiling

Bulk Data Movement

InformaticaComplementing MDM

Hierarchy Management

Event Management

Cross-reference key management

Governance / Audit

DQ Scorecarding & Reporting

Info

rmat

ica

MD

M A

pp

Fuzzy Matching

Pattern Matching Changed Data Capture

MatchingDe-Duplication Publish

Validation

De-Duplication

Matching

Web Services/XML

Validation

FeatureOverlap

Data Standardization

13

Master Data ManagementNew and Grand Vision?

Mainframe

Finance

Sales

Manu

HR

Marketing

Supply

Client/Server / Custom Apps

ERP

SCM

HRM

CRM

Packaged Apps

MDM!

14

CDI PIM FI Hub EDWHomegrown

…or Same Approach with Different Name?

ERP

SCM

HRM

CRM

Mainframe

Finance

Sales

Manu

HR

Marketing

Supply

Client/Server / Custom Apps Packaged Apps

15

Informatica Value Proposition

• Interacting with external systems

• Monitoring and reporting data quality

• Ensuring compliance with rules for data governance

• Integrating with SOA strategies in terms of operating at the heart of "information as a service"

“All the MDM vendors are weak in…”

Gartner Report on MDM

Data Access & Delivery

Data Quality

Data Audit

Data Services

INFO

RM

ATICA

16

Informatica Value PropositionBloor Research Whitepaper

“Put Simply, if you ignore data integration or do not treat it as being of sufficient importance then your MDM project will fail.”

Philip Howard, Bloor Research - October 2006

17

Packaged Applications

Relational DB

Flat Files

Messaging and Web Services

Mainframe and Midrange MDM

…

…

• Interacting with enterprise-wide systems• Profile Data to identify problems• Analyze and share Results

• Standardize and Cleanse• Supplement and Enrich• Test and Load

• Monitor on-going quality• Enforce processes• Scale to other master data types

Assess

MDM Requires High Quality Data“As with any data-driven application, the quality of the data determines the level of success.”

…

18

InformaticaProven Data Foundation for Enterprise Projects…

Packaged Applications

Relational DB

Flat Files

Mainframe & Legacy

EDW

Data Migration

Data Warehousing

ODS

Application Data Synchronization

B2B IntegrationPartners/M&A

Analytics…

• Enterprise-wide data access• Secure and Scalable Platform• OEM technology for Siebel

Analytics, Hyperion, JDE, etc.

Data IntegrationData Quality

19

Packaged Applications

Relational DB

Flat Files

Mainframe & Legacy

EDW

Data Migration

Data Warehousing

ODS

Application Data Synchronization

B2B IntegrationPartners/M&A

Analytics…

…Seamlessly Extended for MDM

MDM

MDM

MDM

20

Data Strategies for MDM Implementation

• Data profiling

• Quantify data quality

• Data Quality Scorecards

Phase 1: Assess Data

• De-risk MDM project

Phase 2: Cleanse & Migrate

• Fix data issues

• Harmonize data for load into MDM application

• Successful MDM data migration and go-live

Phase 3: Sustain and Enforce

• Reuse of data quality rules

• Centralized rule maintenance

• Increase user confidence and adoption

Phase 4: Expand Scope

• Repurpose resources and processes

• Scale project success

Value

ProjectBenefits

MDM Project Lifecycle

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Phase 1: Assess and Quantify Data Issues“You can’t manage what you can’t measure”

Relational DB

Flat Files

Enterprise Data Profiling

Analyze and Score Data’s:Completeness

Conformity

Consistency

Accuracy

Duplicates

Integrity

Benefits• Proactively assess

and discover issues

• Quantify and measure data quality

• Identify and plan for corrective action

• Manage data assets

Packaged Applications

…

22

Packaged Applications

Relational DB

Flat Files

Phase 2: Initial Load / Pilot Project

MDM

…

1. Access source systems

2. Manage Data Quality/Transform Data

4. Target MDM

3. Validate/ Test

Iterate

Directly leverage Assessment results to plan and implement data prior to MDM load

Benefits• Manage Data Quality

• Standardize, Cleanse, Enrich, De-dupe, Validate• Reduce burden on MDM app to clean legacy data

• Bulk data movement

• Complex transformation (no-coding)

Enrich

Hierarchy Management

De-dupe (cross-system)

Merge

Cross Reference Key Management

Audit

Stewardship Tool

Workflow management

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MDM

Phase 3: Sustain and Enforce Data Quality

Benefits• Ongoing reuse and enforcement of Data Quality Rules

either:• External (to MDM App) – DQ for on going

synchronization between data sources and MDM app• Internal (to MDM App) – DQ rules repurposed at the

point of user entry within the MDM app

Assess

Relational DB

Flat Files

Packaged Applications

Bad: Fix Suggested or Record Rejected

Data Quality Service

Good: Committed

Master DataEntry

Master DataEntry

Data Quality

24

Packaged Applications

Relational DB

Flat Files

Phase 4: Enterprise-Wide DeploymentScale Across Systems and Master Data Types

1. Access source systems

2. Manage Data Quality/Transform Data

Synchronization/Bi-directional

Integration

4. Target MDM App

3. Validate/ Test

Iterate

MDM

Benefit• ROI – Reuse of assets and processes• Control Scope – Phased implementation by

sources/applications• Modernize application landscape –

framework to retire legacy applications• Scale MDM scope across data entities

Messaging and Web Services

X

X

Product

Vendor

…

25

KPNMDM Architecture and Project Phases

Customer Registration

System

Sales(SFA)

Customer Data Cleansing Platform

PowerCenter

IDQ

Enterprise Reference Data

ERD

CKRFlat FilesInitial and Scheduled

Loads

ERD Data Load

Data Quality Reports

MDM Staging

Error and Audit Logs

Reference Data for Data Quality Rules

EIMInitial / Bulk

Load

MDM

Cur

rent

Sco

peFu

ture

XML via EAICustomer

Insert / Update

XML via EAICustomer

Insert / Update

XML via EAICustomer

Insert / Update

XML via EAICustomer

Insert / Update

IDQ Match and Merge

XML via EAISFA Insert /

Update

XML via EAISFA Insert /

Update

Phase 1 – Profiling and Assessing DataPhase 2 – Cleanse and Migrate

Phase 3 – Sustain and Enforce

Phase 4 – Expand Scope

26

Informatica Data Quality AssessmentBenefit for MDM customers

• Identify project risks and challenges• Plan and Scope projects based on true state

of data• Promote collaboration between IT and

business owners in identifying and resolving data issues

27

Agenda

• MDM Overview

• Informatica’s Value Prop for MDM

• Case Studies

28

ref123

• Drivers for MDM project:• Lack of data consistency across enterprise landscape (including 8 SAP instances)• Need long term strategy to replace limited MDM solution

• Project Type: • Customer and Supplier (Phase 1); Product (Phase 2)• Currently running a ‘homegrown’ MDM solution - migrating to SAP MDM

• Key Message: • Profiling and Data Quality Management is essential to a successful MDM project.

DuPontDavid O'Donnell

29

DuPontMDM Architecture and Project Phases

Non-SAP

Legacy (+50)

DSAP

W

Q

GT

Profile Reports

IDE

C

KPI

BW

PowerCenter

Keystone(R/3 Instance

for MDM)

Bi-DirectionalLoad & Syndication

IDQ*DQ Rules Repository

Data Quality “Baseline” Scorecards

•Material (MRO)•Chart of Accounts•Vendors•Materials (all types•Customer•BOM•…

IDQ*WSXML XML

SAP PI

SAP MDM

(Pilot Phase)via SAP PI

30

ref123

“MDM is a journey”Geert van Den Berge

Data Management OfficerPhilips Consumer Lifestyle

ref123

PhilipsPeter De Gram

31

ref123

• Drivers for MDM project:• Harmonize product information via GDS (Global Data Synchronization) initiative –

Focus on top 20 business partners (Walmart and Carefour largest ones)• Customers refuse deliveries and penalize Philips (Philips CES losing 25M euros/year)

• Project Type: • Product first (Customer, Employee future phases)• Initial strategy to measure and quantify data quality within business apps and fix there

first• SAP MDM (>28 instances of SAP)

• Key Message: • Data Quality Management can help business create ROI

• e.g. 2.5M euros/year in FTEs just to maintain master data in spreadsheets today)

• IDQ success will help justify and lay foundation for MDM strategy

PhilipsPeter De Gram

3232

Thank You!!

Alf Limalflim@informatica.com