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Master Data Management

Book Description

The key to a successful MDM initiative isn’t technology or methods, it’s people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.

Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you’ll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness.

* Presents a comprehensive roadmap that you can adapt to any MDM project.
* Emphasizes the critical goal of maintaining and improving data quality.
* Provides guidelines for determining which data to “master.”
* Examines special issues relating to master data metadata.
* Considers a range of MDM architectural styles.
* Covers the synchronization of master data across the application infrastructure.

Table of Contents

  1. Cover image
  2. Table of Contents
  3. Praise for Master Data Management
  4. Copyright
  5. Preface
  6. Acknowledgments
  7. About the Author
  8. Chapter 1. Master Data and Master Data Management
  9. 1.1. Driving the Need for Master Data
  10. 1.2. Origins of Master Data
  11. 1.3. What Is Master Data?
  12. 1.4. What Is Master Data Management?
  13. 1.5. Benefits of Master Data Management
  14. 1.6. Alphabet Soup: What about CRM/SCM/ERP/BI (and Others)?
  15. 1.7. Organizational Challenges and Master Data Management
  16. 1.8. MDM and Data Quality
  17. 1.9. Technology and Master Data Management
  18. 1.10. Overview of the Book
  19. 1.11. Summary
  20. Chapter 2. Coordination
  21. 2.1. Introduction
  22. 2.2. Communicating Business Value
  23. 2.3. Stakeholders
  24. 2.4. Developing a Project Charter
  25. 2.5. Participant Coordination and Knowing Where to Begin
  26. 2.6. Establishing Feasibility through Data Requirements
  27. 2.7. Summary
  28. Chapter 3. MDM Components and the Maturity Model
  29. 3.1. Introduction
  30. 3.2. MDM Basics
  31. 3.3. Manifesting Information Oversight with Governance
  32. 3.4. Operations Management
  33. 3.5. Identification and Consolidation
  34. 3.6. Integration
  35. 3.7. Business Process Management
  36. 3.8. MDM Maturity Model
  37. 3.9. Developing an Implementation Road Map
  38. 3.10. Summary
  39. Chapter 4. Data Governance for Master Data Management
  40. 4.1. Introduction
  41. 4.2. What Is Data Governance?
  42. 4.3. Setting the Stage: Aligning Information Objectives with the Business Strategy
  43. 4.4. Data Quality and Data Governance
  44. 4.5. Areas of Risk
  45. 4.6. Risks of Master Data Management
  46. 4.7. Managing Risk through Measured Conformance to Information Policies
  47. 4.8. Key Data Entities
  48. 4.9. Critical Data Elements
  49. 4.10. Defining Information Policies
  50. 4.11. Metrics and Measurement
  51. 4.12. Monitoring and Evaluation
  52. 4.13. Framework for Responsibility and Accountability
  53. 4.14. Data Governance Director
  54. 4.15. Data Governance Oversight Board
  55. 4.16. Data Coordination Council
  56. 4.17. Data Stewardship
  57. 4.18. Summary
  58. Chapter 5. Data Quality and MDM
  59. 5.1. Introduction
  60. 5.2. Distribution, Diffusion, and Metadata
  61. 5.3. Dimensions of Data Quality
  62. 5.4. Employing Data Quality and Data Integration Tools
  63. 5.5. Assessment: Data Profiling
  64. 5.6. Data Cleansing
  65. 5.7. Data Controls
  66. 5.8. MDM and Data Quality Service Level Agreements
  67. 5.9. Influence of Data Profiling and Quality on MDM (and Vice Versa)
  68. 5.10. Summary
  69. Chapter 6. Metadata Management for MDM
  70. 6.1. Introduction
  71. 6.2. Business Definitions
  72. 6.3. Reference Metadata
  73. 6.4. Data Elements
  74. 6.5. Information Architecture
  75. 6.6. Metadata to Support Data Governance
  76. 6.7. Services Metadata
  77. 6.8. Business Metadata
  78. 6.9. Summary
  79. Chapter 7. Identifying Master Metadata and Master Data
  80. 7.1. Introduction
  81. 7.2. Characteristics of Master Data
  82. 7.3. Identifying and Centralizing Semantic Metadata
  83. 7.4. Unifying Data Object Semantics
  84. 7.5. Identifying and Qualifying Master Data
  85. 7.6. Summary
  86. Chapter 8. Data Modeling for MDM
  87. 8.1. Introduction
  88. 8.2. Aspects of the Master Repository
  89. 8.3. Information Sharing and Exchange
  90. 8.4. Standardized Exchange and Consolidation Models
  91. 8.5. Consolidation Model
  92. 8.6. Persistent Master Entity Models
  93. 8.7. Master Relational Model
  94. 8.8. Summary
  95. Chapter 9. MDM Paradigms and Architectures
  96. 9.1. Introduction
  97. 9.2. MDM Usage Scenarios
  98. 9.3. MDM Architectural Paradigms
  99. 9.4. Implementation Spectrum
  100. 9.5. Applications Impacts and Architecture Selection
  101. 9.6. Summary
  102. Chapter 10. Data Consolidation and Integration
  103. 10.1. Introduction
  104. 10.2. Information Sharing
  105. 10.3. Identifying Information
  106. 10.4. Consolidation Techniques for Identity Resolution
  107. 10.5. Classification
  108. 10.6. Consolidation
  109. 10.7. Additional Considerations
  110. 10.8. Summary
  111. Chapter 11. Master Data Synchronization
  112. 11.1. Introduction
  113. 11.2. Aspects of Availability and Their Implications
  114. 11.3. Transactions, Data Dependencies, and the Need for Synchrony
  115. 11.4. Synchronization
  116. 11.5. Conceptual Data Sharing Models
  117. 11.6. Incremental Adoption
  118. 11.7. Summary
  119. Chapter 12. MDM and the Functional Services Layer
  120. 12.1. Collecting and Using Master Data
  121. 12.2. Concepts of the Services-Based Approach
  122. 12.3. Identifying Master Data Services
  123. 12.4. Transitioning to MDM
  124. 12.5. Supporting Application Services
  125. 12.6. Summary
  126. Chapter 13. Management Guidance for MDM
  127. 13.1. Establishing a Business Justification for Master Data Integration and Management
  128. 13.2. Developing an MDM Road Map and Rollout Plan
  129. 13.3. Roles and Responsibilities
  130. 13.4. Project Planning
  131. 13.5. Business Process Models and Usage Scenarios
  132. 13.6. Identifying Initial Data Sets for Master Integration
  133. 13.7. Data Governance
  134. 13.8. Metadata
  135. 13.9. Master Object Analysis
  136. 13.10. Master Object Modeling
  137. 13.11. Data Quality Management
  138. 13.12. Data Extraction, Sharing, Consolidation, and Population
  139. 13.13. MDM Architecture
  140. 13.14. Master Data Services
  141. 13.15. Transition Plan
  142. 13.16. Ongoing Maintenance
  143. 13.17. Summary: Excelsior!
  144. Bibliography and Suggested Reading
  145. Index