six key ingredients for mdm success

The Six Key Ingredients for

Master Data Management

Success

David Loshin,

President, Knowledge Integrity

Daniel Teachey

Director of Corporate Communications, DataFlux Corporation

© 2007 Knowledge Integrity, inc.

www.knowledge-integrity.com

(301)754-6350

2

Principal MDM

Components and Capabilities

David Loshin

Knowledge Integrity, Inc.




(301)754-6350

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Agenda

Introduction to master data management

The MDM Component Layer Model

MDM Maturity

MDM Functional Services

Summary



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The MDM Component Layer Model

Architecture

Identification

Management

Governance

Integration

Business

Process

Management

Master Data Model MDM System Architecture

MDM Service Layer Architecture

Data

Standards

Metadata

Management

Data

Quality

Data

Stewardship

Administration/Configuration

Hierarchy Management Identity Management

Migration Plan

Record Linkage Merging and Consolidation

Identity Search and Resolution

MDM Component Service Layer

Application Integration and Synchronization Service Layer

MDM Business Component Layer

Business Rules

Business Process Integration



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Mixing It Up…

Order of review:

1. Identification

2. Business Process Management

3. Management

4. Governance

5. Integration

6. Architecture



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Identification

Every object subject to “mastering” is managed using a unique representation within the master repository

Any time data intended to refer to that object is seen by an application, its unique representation must be found, verified, and presented back to the application by the MDM platform

IdentificationRecord Linkage Merging and Consolidation




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David Howard Loshin

Loshin, Howard

Howard David LoshinHoward David LoshinHoward David Loshin

Objective: Provide the services that will seek the

matching record in the master index that represents

the “queried” object



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Record Linkage – Parsing and Standardization

Parsing

Identifying and tagging pieces of each data value within a semantic context

Standardization

Correcting terms based on defined rules

Assembling components into recognized patterns

Transformation

Rule-based modifications into target canonical representations

Transformation into target format

Loshin,HowardD LoshinHowardDFirst HowardMiddle DLast Loshin



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Consolidating “Replicated” Data

Knowledge Integrity, Inc. 301-754-6350

Loshin 301-754-6350DavidHoward

Knowledge Integrity Incorporated 301 754-6350

LotionDavid 1163 Kersey Rd



(301)754-6350

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David Loshin

Knowledge Integrity

301-754-6350



(301)754-6350

11






David Loshin

Knowledge Integrity

301-754-6350



(301)754-6350

12






David Loshin

Knowledge Integrity

301-754-6350



(301)754-6350

13






David Loshin

Knowledge Integrity

301-754-6350



(301)754-6350

14

Business Process Management

Business Process Modeling


Business Rules

Business Component Layer

Business

ProcessManagement

MDM Business Component Layer

Business Rules




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Mapping to the Business Processes

BusinessObjectives

BusinessPolicy

BusinessPolicy

BusinessPolicy

BusinessPolicy

Terms

Facts

BusinessLogic

Execution Model

ApplicationApplication





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Business Rules

BusinessPolicy

Business rules contribute to the business process model, and can be isolated and managed as

enterprise content



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Business Component Layer

Services reflecting business process needs

“create new customer”

“locate matching products”

“find purchase order”

“remove supplier”

“modify vendor status”

Business layer built on top of business and component object services

Business process models document the expected ways in which the business operates

The business process model exposes candidate master data objects and the components that use them

Business rules traditionally embedded within application code can be extracted and managed as master content as well



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Management


Hierarchy Management

Identity Management

Migration

Management


Hierarchy Management Identity Management

Migration Plan



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Management Issues

Configuration, administration, and ongoing maintenance

Identity management: For any object, enough identifying information must be managed to determine that

A record exists and no more than one record for the object, or

No record exists and one can be created that can be uniquely distinguished from all others

Hierarchy management – both historical and connectivity

Migration management

Application

MDM



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Governance

Data Standards

Metadata Management

Data Quality

Data Stewardship

GovernanceData

Standards

Metadata

Management

Data

Quality

Data

Stewardship



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Master Object Resolution

Resolution of candidate master data types requires a compete view of what composes the information architecture

This entails cataloging data sets, their attributes, data domains, definitions, contexts, and semantics

This view must facilitate the resolution of:

Format at the element level,

Structure at the instance level, and

Semantics across all levels

This introduces three challenges:

Collecting and analyzing master metadata

Resolving similarity in structure

Understanding and unifying master data semantics



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Operationalizing Data Governance

Actualization of data governance activities enables:

The identification of explicit and hidden risks associated with data expectations

The actualization of the implementation of business policy

Oversight of the definition of critical data elements

Monitoring and auditing information quality rule compliance

Managing enterprise data ownership and stewardship

Coordination and oversight of enterprise data quality

In general, data governance provides management oversight for organizational observance of different kinds of information policies



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Stewardship: Remediation and Manual Intervention

Issues with addressing data quality events:

Immediate remediation of flawed data – does this imply data correction?

Not all data flaws can be captured via automated processes –this implies manual reviews

Accuracy may only be measured by comparing values directly

Carefully integrate manual intervention when necessary in a controlled manner



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Integration

Application integration

Synchronization

Component services

Integration

MDM Component Service Layer

Application Integration and Synchronization Service Layer



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Business

Application

Functional

Interface

Data

Business

Application

Functional

Interface

Data

Wrapper

Facade

Business

Application

Wrapper

Facade

Service

Layer

MDM Repository

Phase 1 Phase 2 Phase 3

Application Transition



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Synchronization and Coherence

Master RepositoryEAI/EII

Federated Consolidated

Issues to consider:

• Frequency of updates to master object attributes• Performance impacts• Bottlenecks

• Attribute overlap



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Component Service – Object Locate

“Object Locate”

Master Index

“Object Factory”



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Architecture

Master data model

MDM system framework

Service layer architecture

Architecture

Master Data Model MDM System Architecture

MDM Service Layer Architecture



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Master Data Model

Limited universe of common master objects

Party, customer, product, part, supplier, claim, instrument

Universal models may be suitable as starting points

Challenges:

Resolution of metadata in a consistent manner

Creating a model that accommodates all applications properly

CUST

First VARCHAR(15)

Middle VARCHAR(15)Last VARCHAR(21)

Address1 VARCHAR(45)Address2 VARCHAR(45)City VARCHAR(30)

State CHAR(2)ZIP CHAR(9)

Nightingale-Patterson

CUSTOMER

FirstName VARCHAR(14)

MiddleName VARCHAR(14)LastName VARCHAR(20)

TelNum NUMERIC(10)

Nightingale-Patterso

Last VARCHAR(21)

LastName VARCHAR(20)



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Central Master/Coexistence



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Registry



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Transaction Hub



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Architecture - Summary

Architecture decisions rely on the requirements identified during the analysis of:

Business processes

Data requirements

Operational processing requirements

Levels of coherence and synchronization

Governance protocols

Component and application services



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Questions?

If you have questions, comments, or suggestions, please contact me

David Loshin

301-754-6350

[email protected]

The Six Key Ingredients for

Master Data Management

Success

Daniel Teachey

Director of Corporate Communications, DataFlux Corporation

About DataFlux

Recognized as a leading provider of data quality and

data integration solutions

Data quality technology to improve corporate

information on customers, products, inventory,

employees, assets…

Over 1,000 customers worldwide

Offices throughout the US and in the UK

Founded in 1997

– Acquired by SAS in 2000

– Operates as a wholly-owned subsidiary

The Methodology for Data Governance

Analyze:The Cost of Bad Data

How is customer retention being affected by poor-

quality data?

How is poor data affecting the ability to do basic

revenue reporting and analysis?

What is the loss from failed business transactions

due to data issues?

How quickly can we respond to business

opportunities?

Analyze:Discover Poor-Quality Data

Create a scorecard of

current data quality

Build a plan to address

pressing data quality

issues

Develop reports for

management to detail

data quality problems

Improve:Solve Data Quality Issues

Problem Solution

Multiple occurrences of the same

customer (duplicate names and/or

addresses)

Create match conditions for

customers and apply the rule to all

enterprise applications

Thousands of duplicate,

unclassified products

Standardize product and item data

on uniform classifications

(UNSPSC)

No validation at point of entry Utilize business rules for data

quality as real-time services via

SOA architecture

Improve:Build Data Quality Rules

Create a business rule to

resolve data quality

Build jobs to standardize,

integrate and enhance

information

Use jobs in batch or real-time

processes

Control:Monitor Data Quality

Ensure information accuracy

Validate data against your business rules

Automatically identify invalid data

Control:Enforce Enterprise Standards

Enforce business

rules and data

governance

standards

Check the quality of

product information

over time

Create dashboards

to help track data

quality issues

six key ingredients for mdm success

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