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Master Data Management

Book Description

The key to a successful MDM initiative isnt technology or methods, its 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 MDMan understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: youll 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. Praise for Master Data Management
  2. Copyright
  3. Preface
    1. About the Approach Described in This Book
    2. Overview of the Book
    3. More about MDM and Contact Information
  4. Acknowledgments
  5. About the Author
  6. 1. Master Data and Master Data Management
    1. 1.1. Driving the Need for Master Data
    2. 1.2. Origins of Master Data
      1. 1.2.1. Example: Customer Data
    3. 1.3. What Is Master Data?
    4. 1.4. What Is Master Data Management?
    5. 1.5. Benefits of Master Data Management
    6. 1.6. Alphabet Soup: What about CRM/SCM/ERP/BI (and Others)?
    7. 1.7. Organizational Challenges and Master Data Management
    8. 1.8. MDM and Data Quality
    9. 1.9. Technology and Master Data Management
    10. 1.10. Overview of the Book
    11. 1.11. Summary
  7. 2. Coordination: Stakeholders, Requirements, and Planning
    1. 2.1. Introduction
    2. 2.2. Communicating Business Value
      1. 2.2.1. Improving Data Quality
      2. 2.2.2. Reducing the Need for Cross-System Reconciliation
      3. 2.2.3. Reducing Operational Complexity
      4. 2.2.4. Simplifying Design and Implementation
      5. 2.2.5. Easing Integration
    3. 2.3. Stakeholders
      1. 2.3.1. Senior Management
      2. 2.3.2. Business Clients
      3. 2.3.3. Application Owners
      4. 2.3.4. Information Architects
      5. 2.3.5. Data Governance and Data Quality
      6. 2.3.6. Metadata Analysts
      7. 2.3.7. System Developers
      8. 2.3.8. Operations Staff
    4. 2.4. Developing a Project Charter
    5. 2.5. Participant Coordination and Knowing Where to Begin
      1. 2.5.1. Processes and Procedures for Collaboration
      2. 2.5.2. RACI Matrix
      3. 2.5.3. Modeling the Business
      4. 2.5.4. Consensus Driven through Metadata
      5. 2.5.5. Data Governance
    6. 2.6. Establishing Feasibility through Data Requirements
      1. 2.6.1. Identifying the Business Context
      2. 2.6.2. Conduct Stakeholder Interviews
      3. 2.6.3. Synthesize Requirements
      4. 2.6.4. Establishing Feasibility and Next Steps
    7. 2.7. Summary
  8. 3. MDM Components and the Maturity Model
    1. 3.1. Introduction
    2. 3.2. MDM Basics
      1. 3.2.1. Architecture
      2. 3.2.2. Master Data Model
      3. 3.2.3. MDM System Architecture
      4. 3.2.4. MDM Service Layer Architecture
    3. 3.3. Manifesting Information Oversight with Governance
      1. 3.3.1. Standardized Definitions
      2. 3.3.2. Consolidated Metadata Management
      3. 3.3.3. Data Quality
      4. 3.3.4. Data Stewardship
    4. 3.4. Operations Management
      1. 3.4.1. Identity Management
      2. 3.4.2. Hierarchy Management and Data Lineage
      3. 3.4.3. Migration Management
      4. 3.4.4. Administration/Configuration
    5. 3.5. Identification and Consolidation
      1. 3.5.1. Identity Search and Resolution
      2. 3.5.2. Record Linkage
      3. 3.5.3. Merging and Consolidation
    6. 3.6. Integration
      1. 3.6.1. Application Integration with Master Data
      2. 3.6.2. MDM Component Service Layer
    7. 3.7. Business Process Management
      1. 3.7.1. Business Process Integration
      2. 3.7.2. Business Rules
      3. 3.7.3. MDM Business Component Layer
    8. 3.8. MDM Maturity Model
      1. 3.8.1. Initial
      2. 3.8.2. Reactive
      3. 3.8.3. Managed
      4. 3.8.4. Proactive
      5. 3.8.5. Strategic Performance
    9. 3.9. Developing an Implementation Road Map
    10. 3.10. Summary
  9. 4. Data Governance for Master Data Management
    1. 4.1. Introduction
    2. 4.2. What Is Data Governance?
    3. 4.3. Setting the Stage: Aligning Information Objectives with the Business Strategy
      1. 4.3.1. Clarifying the Information Architecture
      2. 4.3.2. Mapping Information Functions to Business Objectives
      3. 4.3.3. Instituting a Process Framework for Information Policy
    4. 4.4. Data Quality and Data Governance
    5. 4.5. Areas of Risk
      1. 4.5.1. Business and Financial
      2. 4.5.2. Reporting
      3. 4.5.3. Entity Knowledge
      4. 4.5.4. Protection
      5. 4.5.5. Limitation of Use
    6. 4.6. Risks of Master Data Management
      1. 4.6.1. Establishing Consensus for Coordination and Collaboration
      2. 4.6.2. Data Ownership
      3. 4.6.3. Semantics: Form, Function, and Meaning
    7. 4.7. Managing Risk through Measured Conformance to Information Policies
    8. 4.8. Key Data Entities
    9. 4.9. Critical Data Elements
    10. 4.10. Defining Information Policies
    11. 4.11. Metrics and Measurement
    12. 4.12. Monitoring and Evaluation
    13. 4.13. Framework for Responsibility and Accountability
    14. 4.14. Data Governance Director
    15. 4.15. Data Governance Oversight Board
    16. 4.16. Data Coordination Council
    17. 4.17. Data Stewardship
    18. 4.18. Summary
  10. 5. Data Quality and MDM
    1. 5.1. Introduction
    2. 5.2. Distribution, Diffusion, and Metadata
    3. 5.3. Dimensions of Data Quality
      1. 5.3.1. Uniqueness
      2. 5.3.2. Accuracy
      3. 5.3.3. Consistency
      4. 5.3.4. Completeness
      5. 5.3.5. Timeliness
      6. 5.3.6. Currency
      7. 5.3.7. Format Compliance
      8. 5.3.8. Referential Integrity
    4. 5.4. Employing Data Quality and Data Integration Tools
    5. 5.5. Assessment: Data Profiling
      1. 5.5.1. Profiling for Metadata Resolution
      2. 5.5.2. Profiling for Data Quality Assessment
      3. 5.5.3. Profiling as Part of Migration
    6. 5.6. Data Cleansing
    7. 5.7. Data Controls
      1. 5.7.1. Data and Process Controls
      2. 5.7.2. Data Quality Control versus Data Validation
    8. 5.8. MDM and Data Quality Service Level Agreements
      1. 5.8.1. Data Controls, Downstream Trust, and the Control Framework
    9. 5.9. Influence of Data Profiling and Quality on MDM (and Vice Versa)
    10. 5.10. Summary
  11. 6. Metadata Management for MDM
    1. 6.1. Introduction
    2. 6.2. Business Definitions
      1. 6.2.1. Concepts
      2. 6.2.2. Business Terms
      3. 6.2.3. Definitions
      4. 6.2.4. Semantics
    3. 6.3. Reference Metadata
      1. 6.3.1. Conceptual Domains
      2. 6.3.2. Value Domains
      3. 6.3.3. Reference Tables
      4. 6.3.4. Mappings
    4. 6.4. Data Elements
      1. 6.4.1. Critical Data Elements
      2. 6.4.2. Data Element Definition
      3. 6.4.3. Data Formats
      4. 6.4.4. Aliases/Synonyms
    5. 6.5. Information Architecture
      1. 6.5.1. Master Data Object Class Types
      2. 6.5.2. Master Entity Models
      3. 6.5.3. Master Object Directory
      4. 6.5.4. Relational Tables
    6. 6.6. Metadata to Support Data Governance
      1. 6.6.1. Information Usage
      2. 6.6.2. Information Quality
      3. 6.6.3. Data Quality SLAs
      4. 6.6.4. Access Control
    7. 6.7. Services Metadata
      1. 6.7.1. Service Directory
      2. 6.7.2. Service Users
      3. 6.7.3. Interfaces
    8. 6.8. Business Metadata
      1. 6.8.1. Business Policies
      2. 6.8.2. Information Policies
      3. 6.8.3. Business Rules
    9. 6.9. Summary
  12. 7. Identifying Master Metadata and Master Data
    1. 7.1. Introduction
    2. 7.2. Characteristics of Master Data
      1. 7.2.1. Categorization and Hierarchies
      2. 7.2.2. Top-Down Approach: Business Process Models
      3. 7.2.3. Bottom-Up Approach: Data Asset Evaluation
    3. 7.3. Identifying and Centralizing Semantic Metadata
      1. 7.3.1. Example
      2. 7.3.2. Analysis for Integration
      3. 7.3.3. Collecting and Analyzing Master Metadata
      4. 7.3.4. Resolving Similarity in Structure
    4. 7.4. Unifying Data Object Semantics
    5. 7.5. Identifying and Qualifying Master Data
      1. 7.5.1. Qualifying Master Data Types
      2. 7.5.2. The Fractal Nature of Metadata Profiling
      3. 7.5.3. Standardizing the Representation
    6. 7.6. Summary
  13. 8. Data Modeling for MDM
    1. 8.1. Introduction
    2. 8.2. Aspects of the Master Repository
      1. 8.2.1. Characteristics of Identifying Attributes
      2. 8.2.2. Minimal Master Registry
      3. 8.2.3. Determining the Attributes Called “Identifying Attributes”
    3. 8.3. Information Sharing and Exchange
      1. 8.3.1. Master Data Sharing Network
      2. 8.3.2. Driving Assumptions
      3. 8.3.3. Two Models: Persistence and Exchange
    4. 8.4. Standardized Exchange and Consolidation Models
      1. 8.4.1. Exchange Model
      2. 8.4.2. Using Metadata to Manage Type Conversion
      3. 8.4.3. Caveat: Type Downcasting
    5. 8.5. Consolidation Model
    6. 8.6. Persistent Master Entity Models
      1. 8.6.1. Supporting the Data Life Cycle
      2. 8.6.2. Universal Modeling Approach
      3. 8.6.3. Data Life Cycle
    7. 8.7. Master Relational Model
      1. 8.7.1. Process Drives Relationships
      2. 8.7.2. Documenting and Verifying Relationships
      3. 8.7.3. Expanding the Model
    8. 8.8. Summary
  14. 9. MDM Paradigms and Architectures
    1. 9.1. Introduction
    2. 9.2. MDM Usage Scenarios
      1. 9.2.1. Reference Information Management
      2. 9.2.2. Operational Usage
      3. 9.2.3. Analytical Usage
    3. 9.3. MDM Architectural Paradigms
      1. 9.3.1. Virtual/Registry
      2. 9.3.2. Transaction Hub
      3. 9.3.3. Hybrid/Centralized Master
    4. 9.4. Implementation Spectrum
    5. 9.5. Applications Impacts and Architecture Selection
      1. 9.5.1. Number of Master Attributes
      2. 9.5.2. Consolidation
      3. 9.5.3. Synchronization
      4. 9.5.4. Access
      5. 9.5.5. Service Complexity
      6. 9.5.6. Performance
    6. 9.6. Summary
  15. 10. Data Consolidation and Integration
    1. 10.1. Introduction
    2. 10.2. Information Sharing
      1. 10.2.1. Extraction and Consolidation
      2. 10.2.2. Standardization and Publication Services
      3. 10.2.3. Data Federation
      4. 10.2.4. Data Propagation
    3. 10.3. Identifying Information
      1. 10.3.1. Indexing Identifying Values
      2. 10.3.2. The Challenge of Variation
    4. 10.4. Consolidation Techniques for Identity Resolution
      1. 10.4.1. Identity Resolution
      2. 10.4.2. Parsing and Standardization
      3. 10.4.3. Data Transformation
      4. 10.4.4. Normalization
      5. 10.4.5. Matching/Linkage
      6. 10.4.6. Approaches to Approximate Matching
      7. 10.4.7. The Birthday Paradox versus the Curse of Dimensionality
    5. 10.5. Classification
      1. 10.5.1. Need for Classification
      2. 10.5.2. Value of Content and Emerging Techniques
    6. 10.6. Consolidation
      1. 10.6.1. Similarity Thresholds
      2. 10.6.2. Survivorship
      3. 10.6.3. Integration Errors
      4. 10.6.4. Batch versus Inline
      5. 10.6.5. History and Lineage
    7. 10.7. Additional Considerations
      1. 10.7.1. Data Ownership and Rights of Consolidation
      2. 10.7.2. Access Rights and Usage Limitations
      3. 10.7.3. Segregation Instead of Consolidation
    8. 10.8. Summary
  16. 11. Master Data Synchronization
    1. 11.1. Introduction
    2. 11.2. Aspects of Availability and Their Implications
    3. 11.3. Transactions, Data Dependencies, and the Need for Synchrony
      1. 11.3.1. Data Dependency
      2. 11.3.2. Business Process Considerations
      3. 11.3.3. Serializing Transactions
    4. 11.4. Synchronization
      1. 11.4.1. Application Infrastructure Synchronization Requirements
    5. 11.5. Conceptual Data Sharing Models
      1. 11.5.1. Registry Data Sharing
      2. 11.5.2. Repository Data Sharing
      3. 11.5.3. Hybrids and Federated Repositories
      4. 11.5.4. MDM, the Cache Model, and Coherence
    6. 11.6. Incremental Adoption
      1. 11.6.1. Incorporating and Synchronizing New Data Sources
      2. 11.6.2. Application Adoption
    7. 11.7. Summary
  17. 12. MDM and the Functional Services Layer
    1. 12.1. Collecting and Using Master Data
      1. 12.1.1. Insufficiency of ETL
      2. 12.1.2. Replication of Functionality
      3. 12.1.3. Adjusting Application Dependencies
      4. 12.1.4. Need for Architectural Maturation
      5. 12.1.5. Similarity of Functionality
    2. 12.2. Concepts of the Services-Based Approach
    3. 12.3. Identifying Master Data Services
      1. 12.3.1. Master Data Object Life Cycle
      2. 12.3.2. MDM Service Components
      3. 12.3.3. More on the Banking Example
      4. 12.3.4. Identifying Capabilities
    4. 12.4. Transitioning to MDM
      1. 12.4.1. Transition via Wrappers
      2. 12.4.2. Maturation via Services
    5. 12.5. Supporting Application Services
      1. 12.5.1. Master Data Services
      2. 12.5.2. Life Cycle Services
      3. 12.5.3. Access Control
      4. 12.5.4. Integration
      5. 12.5.5. Consolidation
      6. 12.5.6. Workflow/Rules
    6. 12.6. Summary
  18. 13. Management Guidance for MDM
    1. 13.1. Establishing a Business Justification for Master Data Integration and Management
    2. 13.2. Developing an MDM Road Map and Rollout Plan
      1. 13.2.1. MDM Road Map
      2. 13.2.2. Rollout Plan
    3. 13.3. Roles and Responsibilities
    4. 13.4. Project Planning
    5. 13.5. Business Process Models and Usage Scenarios
    6. 13.6. Identifying Initial Data Sets for Master Integration
    7. 13.7. Data Governance
    8. 13.8. Metadata
    9. 13.9. Master Object Analysis
    10. 13.10. Master Object Modeling
    11. 13.11. Data Quality Management
    12. 13.12. Data Extraction, Sharing, Consolidation, and Population
    13. 13.13. MDM Architecture
    14. 13.14. Master Data Services
    15. 13.15. Transition Plan
    16. 13.16. Ongoing Maintenance
    17. 13.17. Summary: Excelsior!
  19. Bibliography
    1. Bibliography and Suggested Reading