MDM Architecture and Implementation Best Practices

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MDM Architecture and Implementation

Best Practices

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Agenda

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Fragmented Data Problems …



Master Data & Master Data Management

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IBM Infosphere Master Data Management Server



MDM Server Implementation Pattern



MDM Architecture

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MDM Data Stewardship Application

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Islands of Information

Reduce customer satisfaction, decrease revenue, hinder relationships

Document Management Michael Johnson Mortgage.tif _ERP JP Morgan, USA Cust ID : JP003 CRM Call Center Mike Johnson JP Morgan Chase Last Interaction: 4/11/03 (product not received) Retail Point of Sale

JP Morgan & Chase Contact : Michael A

Johnson, CIO 270 West St, NY

Portal

Michael Johnson User ID: Mjohnso ! Personalized access ! Gold Customer ! Sub: Newsletter 1 CRM Marketing Michael Johnson ! Opt-Out flag ! No Promotion flag Application Forms Payments Service and Support Online Registration In-Store Interactions Direct Mail Response Data Warehouse Michael P Johnson 1400 54rd Avenue NY NY 212 995-3345 3rd_Party Information Internet Commerce Michael Johnson User id :mjohnson JP Morgan Contract:: JP987 Online Purchases

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Coffee Beans GTIN 20012294219421 CAN Code : 21204 USA Code : 21192 BR, CR, MEX Code : 21186

CH, AUT, DE, UK, FR, BEL, NL, IT : Code : 21184

DE, FIN, SWE, NOR, ESP, POR, Code : 21190

BUL, YUG, CR, RO, SLOV Code : 19616 ISR Code : 21204 MidEa Code : 21204 World Trade Code : 19619, 19616 AUS Code : 21190

HK, TAI, SIN, MAL, S.KOR Code : 21188

JAP, THAI, INDO, PHI Code : 21189

CZ, LIT, EST, SLOV, RU Code : 2002494

Inconsistent Master Information is a Major Hurdle

Impacts Revenue, Cost, Agility and Compliance

ARG

Code : 21184

Gaining control over product information results:

Errors in data –30% of data in retailers systems is wrong

Lost productiv ity –25 minutes manual cleansing per SKU, per year

Slow time to market –4 weeks to introduce new products

Inv oice deductions –43% of invoices result in deductions

Failed scans –up to 70,000 per week (1 large US Retailer)

Lost sales –up to 3.5% per year

Source: A.T. Kearney, GMA, AMR Industry Driv ers:RFID, Waste Electrical and Electronic Equipment Recycling, Product Information Exchange Standards, Return of Hazardous Substances,

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Master Data

Facts describing the core business entities: customers, suppliers,

partners, products, materials, bill of materials, chart of

accounts, location and employees

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The high value information an organization uses repeatedly

across many business processes

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Generally used across multiple LOB

Master Data Management

Master Data Management (MDM) is the set of disciplines,

technologies and solutions to create and maintain consistent,

complete, contextual and accurate business data for all

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MDM Repository / Hub is analogous to a Version Control System,

albeit for your Master Data

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MDM Server Implementation Environment

MDM Domains Business Servi ces

Pre- buil t Cust omi zable

Data Stewardshi p

Party Account Product C ustom

Integratio n

Content Dat a Analy tic s

InfoSphere MDM Server

I nfoSphere M DM Server

MDM Domains Business Servi ces

Pre- buil t Cust omi zable

Data Stewardshi p

Party Account Product C ustom

Integratio n

Content Dat a Analy tic s

InfoSphere MDM Server

I nfoSphere M DM Server

RDBMS

Security, Mail, etc

Real-time/Near-Real-Time Connectivity Services (ESB, EAI, Web Services, MQ, etc.)

Enterprise Data Integration (InfoServer)

Billing/ Provisioning

Call Center

W eb Phone Sales Vendor & Other

Business Partners Management

Service

Master Data Batch Load

External Data Providers (e.g. D&B, ACXIOM,

Experian)

Enterprise

Data Warehouse/ Data Mart

Corporate & Others Content Management Process Server

Understand Cleanse Transform Deliv er

Order Customer Product Account Others Customer Product Account Others Customer Product Account Others Customer Product Account Others New Systems (e.g. SOA –based)

(e.g. Siebel) (e.g COGNOS)

Real-time

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Understand Cleanse Transform Deliver Unified Deployment

ETL Tooling

Unified Metadata Management

The Complete Picture – Multi Form MDM Server

Multiform MDM manages data domains critical to business processes Multiform MDM leverages merged, cleansed and standardized

IBM Master Data Management

Banking Insurance Government Healthcare Retail Telco

Focused on critical information intensive business problems In d u s tr y Mo d e ls & A s s e ts

Multiform Master Data Management

Collaborate Operationalize Analyze

Party (Customer, citizen, prospect, organization, supplier, distributor, etc.)

Product (good, service, product bundle, catalogue, product component, etc.)

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A dmin Web A pp D ata Stewardship Web A pp Web Services A dapter Batc h P roc essor E vent M anager E SB / M Q / E A I Broker D as hboard/ P ortal U Is C lient A pplications MDM Consumers

Service Controller _{P arser}

C onstructor

Request Framework

Bus iness Transaction Manager Bus iness Proxies

MDM Core

Utility Components

Bus iness L ogic C omponents E xtension C ontroller Java Classes Rule Sets Rules Engine Pre/Post Txn Pre/Post Action Extension Framework Common Components Standardization Audit Data Metadata Error Messages Data-Level Entitlements M etaData E vent M anager P erformance T rac ker E rror M es saging L ogging T ransaction Audit I nformation L og E xternal V alidation E xternal Bus iness Rules

Rules of V isibility C onfiguration M anager Rules Engine Logs ARM Agent Configuration Settings Validation Rules XM L C omposite T ransaction H andler Java Classes Rule Sets M es saging A dapter N otification Request H andler C ontroller Components Business Services Admin Services Party Services Contract Services History Services Fast Track Sec urity Events Validators E vergreen P roc essor Suspect Processing Components Notifications Adapters

Web Services As ynchronous Synchronous Client-Defined I nterface

Data WC C C o r e Operational Tables History Data History Tables Rule Data Code Tables Data Extension Tables Behavior Extensions Data Extensions JMS T opic E xtension T oolkit

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 Step 1: Optimizes data for statistical comparisons

– Normalizes & compacts data, creates derived data layer source data remains intact

– Phonetic equivalences, tokenization, nicknames, etc.

 Step 2: Finds all the potential matches

– Casts a wide net – all matches on current or historical attributes, prevents misses

– Partial matches, reversals, anonymous values, etc.

 Step 3: Scores accurately via probabilistic statistics

– Compares attributes one-by-one and produces a weighted score (likelihood ratio) for each pair of records

– Frequency weights specific to your business – Edit distance, proximity of match

 Step 4: Custom threshold settings – Single or dual threshold models – Link, don’t link, don’t know – “learns”

from manual input

Manual review Manual review Lowest possible score Lowest possible score Highest possible score Highest possible score Don’t link Don’t

link LinkLink

Should be linked Should not be linked

Name 1222 2222 3334

Data Derived Hash

Buckets Robert RBT 121213444 Potter PTR 34839020 ZIP 3456 6666 5435 SSN 3421 3333 3555

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9

8.63

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Useful Links

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IBM Information On Demand website

(http://www-306.ibm.com/software/data/information-on-demand)

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IBM Information Management website

(http://www.ibm.com/software/data)

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Extreme Leverage website

(http://w3-103.ibm.com/software/xl/portal/viewcontent?type=doc&srcID=XT&docID=B329727F31168I32)

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Customer Success Stories

(http://www-306.ibm.com/software/success/cssdb.nsf/topstoriesFM?OpenForm&Site=db2software)

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Information Management Demos

(http://demos.dfw.ibm.com/solutions/infomgmt/)

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Information as a Service Demo

(http://media.dvdpowertools.com/ibm/infomanagement/interface.php)

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Videos