Contents

Forewords

Acknowledgments

Introduction

Part I Introduction to Master Data Management

1 Overview of Master Data Management

Master Data Management (MDM)

Defining Master Data

Why Master Data Management Now?

Challenges of Creating and Managing Master Data

Defining Master Data Management

Master Data Management for Customer Domain:Customer Data Integration (CDI)

Evolution of MDM and CDI

Other MDM Variants: Products, Organizations, Hierarchies

Challenges of MDM Implementation for Product Domain

Introduction to MDM Classification Dimensions

Key Benefits of Master Data Management

References

2 MDM: Overview of Market Drivers and Key Challenges

Market Growth and Adoption of MDM

MDM Growth and Customer Centricity

Business and Operational Drivers of MDM

Improving Customer Experience

Improving Customer Retention and Reducing Attrition Rates

Growing Revenue by Leveraging Customer Relationships

Improving Customer Service Time:Just-in-Time Information Availability

Improving Marketing Effectiveness

Reducing Administrative Process Costs and Inefficiencies

Reducing Information Technology Maintenance Costs

MDM Challenges

Senior Management Commitment and Value Proposition

Customer Centricity and a 360-Degree View of a Customer

Challenges of Selling MDM Inside the Enterprise

Socializing MDM as a Multidimensional Challenge

Technical Challenges of MDM

Implementation Costs and Time-to-Market Concerns

Data Quality, Data Synchronization, and Integration Challenges

Data Visibility, Security, and Regulatory Compliance

Challenges of Global MDM Implementations

References

3 MDM Applications by Industry

Industry Views of MDM

Commercial Sector

Financial Services, Banking, and Insurance

Telecommunications Industry

Healthcare Services Ecosystem

Hospitality and Gaming Industry

Manufacturing and Software

Pharmaceutical Industry

Shipping and Logistics

Airlines

Retail Sales

Public Sector

Social Services

Law Enforcement, Border Protection, and Intelligence Agencies

References

Part II Architectural Considerations

4 MDM Architecture Classifications, Concepts, Principles, and Components

Architectural Definition of Master Data Management

Evolution of Master Data Management Architecture

MDM Architectural Philosophy and Key Architecture Principles

Enterprise Architecture Framework: A Brief Introduction

MDM Architecture Viewpoints

Services Architecture View

Architecture Viewpoints of Various MDM Classification Dimensions

Reference Architecture Viewpoint

References

5 Data Management Concerns of MDM Architecture: Entities, Hierarchies, and Metadata

Data Strategy

Guiding Principles of Information Architecture

Data Governance

Data Stewardship and Ownership

Data Quality

Data Quality Tools and Technologies

Managing Data in the Data Hub

Data Zone Architecture Approach

Operational and Analytical MDM and Data Zones

Loading Data into the Data Hub

Data Synchronization

Overview of Business Rules Engines

Data Delivery and Metadata Concerns

Enterprise Information Integration and Integrated Data Views

References

6 MDM Services for Entity and Relationships Resolution and Hierarchy Management

Architecting an MDM System for Entity Resolution

Recognizing Individuals, Groups, and Relationships

MDM and Party Data Model

Entity Groupings and Hierarchies

Challenge of Product Identification, Recognition, and Linking

MDM Architecture for Entity Resolution

Key Services and Capabilities for Entity Resolution

Entity Resolution and MDM Reference Architecture

Entity Recognition, Matching, and Generation of Unique Identifiers

Matching and Linking Services and Techniques

Aggregating Entity Information

Data Hub Keys and Life-Cycle Management Services

Key Management and Key Generation Service

Record Locator Services

References

7 Master Data Modeling

Importance of Data Modeling

Predominant Data Modeling Styles

MDM Data Modeling Requirements

Data Modeling Styles and Their Support for Multidomain MDM

Approach 1: The “Right” Data Model

Approach 2: Metadata Model

Approach 3: Abstract MDM-Star Model

References

Part III Data Security, Privacy, and Regulatory Compliance

8 Overview of Risk Management for Master Data

Risk Taxonomy

Regulatory Compliance Landscape

Integrated Risk Management: Benefits and Challenges

Regulatory Compliance Requirements and Their Impact on MDM IT Infrastructure

The Sarbanes-Oxley Act

Gramm-Leach-Bliley Act Data Protection Provisions

Other Regulatory/Compliance Requirements

Key Information Security Risks and Regulatory Concerns

Identity Theft

GLBA, FCRA, Privacy, and Opt-Out

Key Technical Implications of Data Security and Privacy Regulations on MDM Architecture

References

9 Introduction to Information Security and Identity Management

Traditional and Emerging Concerns of Information Security

What Do We Need to Secure?

End-to-End Security Framework

Traditional Security Requirements

Emerging Security Requirements

Overview of Security Technologies

Confidentiality and Integrity

Network and Perimeter Security Technologies

Secure HTTP Protocols/SSL/TLS/WTLS

Application, Data, and User Security

Integrating Authentication and Authorization

SSO Technologies

Web Services Security Concerns

Authentication

Data Integrity and Confidentiality

Attacks

WS-Security Standard

Putting It All Together

References

10 Protecting Content for Secure Master Data Management

Data Security Evolution

Emerging Information Security Threats

Regulatory Drivers for Data Protection

Risks of Data Compromise

Technical Implications of Data Security Regulations

Data Security Overview

Layered Security Framework

Data-in-Transit Security Considerations

Data-at-Rest Protection

Enterprise Rights Management

ERM Processes and MDM Technical Requirements

ERM Use Case Examples

References

11 Enterprise Security and Data Visibility in Master Data Management Environments

Access Control Basics

Groups and Roles

Roles-Based Access Control (RBAC)

Policies and Entitlements

Entitlements Taxonomy

Transactional Entitlements

Entitlements and Visibility

Customer Data Integration Visibility Scenario

Policies, Entitlements, and Standards

XACML

Integrating MDM Solutions with Enterprise Information Security

Overview of Key Architecture Components for Policy Decision and Enforcement

Integrated Conceptual Security and Visibility Architecture

References

Part IV Implementing and Governing Master Data Management

12 Building a Business Case and Roadmap for MDM

Importance of the MDM Business Case and the Current State of the Problem

MDM Sponsorship Scenarios and Their Challenges

Business Strategy–Driven MDM

IT Strategy–Driven MDM

What MDM Stakeholders Want to Know

Business Processes and MDM Drivers

Benefits and Their Estimation

Traditional Methods for Estimation of Business Benefits

Economic Value of Information as MDM Business Case Estimation Technique

Importance of the MDM Roadmap

Basic MDM Costs

MDM Roadmap Views

Conclusion

References

13 Project Initiation

Implementation Begins

Addressing the Complexity of MDM Projects

Scope Definition

Business Processes

Lines of Business and Functions

Customer Touch Points, Product Types, and Account Types

Levels of Aggregation and Relationship Types

Entities and Attributes

Systems and Applications in Scope

MDM Data Hub Solution Architecture

Data Hub Architecture Styles

Phased Implementation of Customer Data Hub

Artifacts That Should Be Produced in the Project Initiation Phase

Project Work Streams

References

14 Entity Resolution: Identification, Matching, Aggregation, and Holistic View of the Master Objects

Holistic Entity View and a 360-Degree View of a Customer: Frequently Used Terms and Definitions

Reasons for False Positives in Party Matching

Reasons for False Negatives in Party Matching

Attributes and Attribute Categories Commonly Used for Matching and Identification

Identity Attributes

Discriminating Attributes

Record Qualification Attributes

Customer Identification, Matching Process, and Models

Minimum Data Requirements

Matching Modes

Defining Matching Rules for Customer Records

Effect of Chaining

Break Groups and Performance Considerations

Similarity Libraries and Fuzzy Logic for Attribute Comparisons

Summary of Data-Matching Requirements and Solutions

References

15 Beyond Party Match: Merge, Split, Party Groups, and Relationships

Merge and Split

Merge

Split

Relationships and Groups

Direct Business Relationships with an Individual

Households and Groups

Relationship Challenges of Institutional Customers and Contacts

Relationship Challenges of Institutional Customers

Need for Persistent Match Group Identifiers

Additional Considerations for Customer Identifiers

References

16 Data Synchronization, MDM System Testing, and Other Implementation Concerns

Goals of Data Synchronization

Technology Approach to Use Case Realization

MDM Data Hub with Multiple Points of Entry for Entity Information

Considerations for the Transaction Hub Master Model

Batch Processing

Synchronization and Considerations for Exceptions Processing

Testing Considerations

Testing of MDM Data and Services

Testing MDM Services

Creation and Protection of Test Data

Considerations for the MDM Application and Presentation Layers

Data Hub Management, Configuration, and Administration Applications

Reporting

Additional Technical and Operational Concerns

Environment and Infrastructure Considerations

Deployment

Considerations for the MDM Data Hub Data Model and Services

References

17 Master Data Governance

Basics of Data Governance

Introduction to and History of Data Governance

Definitions of Data Governance

Data Governance Frameworks, Focus Areas, and Capability Maturity Model

Data Governance for Master Data Management

Data Quality Management

Data Quality Processes

Master Data Governance Policies for Data Quality

Master Data Governance Metrics for Information Quality

The Existing Approaches to Quantifying Data Quality

Information Theory Approach to Data Quality for MDM

The Use of Matching Algorithm Metrics

How to Make Data Governance More Focused and Efficient

Agile Data Governance

Overlaps with Business Requirements Generated by Departments and Business Functions

Overlaps with the Enterprise IT

Data Governance and Data Governance Frameworks

Processes and Metrics

Data Governance Software

Data Governance and Edward Deming Principles

Conclusion

References

Part V Master Data Management: Markets, Trends, and Directions

18 MDM Vendors and Products Landscape

MDM Market Consolidation

Major MDM Vendors

IBM

Oracle

Informatica

SAP

SAS DataFlux

Tibco

Dun & Bradstreet Purisma

Acxiom

References

19 Where Do We Go from Here?

Review of the Key Points Covered in the Preceding Chapters

A Brief Summary of Lessons Learned

Main Reasons MDM Projects Fail

Review of the Key Reasons for MDM Project Failure

MDM Guiding Principles

Master Data Management: Trends and Directions

MDM Market Trends

MDM Technical Capabilities Trends

References

Part VI Appendixes

A List of Acronyms

B Glossary

Index

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