Cover image for Master Data Management and Data Governance

Master Data Management and Data Governance

Author Larry Dubov , Alex Berson
Release Date: 2010/11/01
ISBN: 9780071744584
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Book Description

The latest techniques for building a customer-focused enterprise environment

"The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc.

Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume.

• Plan and implement enterprise-scale MDM and Data Governance solutions

• Develop master data model

• Identify, match, and link master records for various domains through entity resolution

• Improve efficiency and maximize integration using SOA and Web services

• Ensure compliance with local, state, federal, and international regulations

• Handle security using authentication, authorization, roles, entitlements, and encryption

• Defend against identity theft, data compromise, spyware attack, and worm infection

• Synchronize components and test data quality and system performance

Table of Contents

  1. Cover Page
  2. Master Data Management and Data Governance
  3. Copyright Page
  4. Contents
  5. Forewords
  6. Acknowledgments
  7. Introduction
  8. Part I Introduction to Master Data Management
    1. 1 Overview of Master Data Management
      1. Master Data Management (MDM)
        1. Defining Master Data
        2. Why Master Data Management Now?
        3. Challenges of Creating and Managing Master Data
        4. Defining Master Data Management
      2. Master Data Management for Customer Domain:Customer Data Integration (CDI)
        1. Evolution of MDM and CDI
      3. Other MDM Variants: Products, Organizations, Hierarchies
        1. Challenges of MDM Implementation for Product Domain
      4. Introduction to MDM Classification Dimensions
      5. Key Benefits of Master Data Management
      6. References
    2. 2 MDM: Overview of Market Drivers and Key Challenges
      1. Market Growth and Adoption of MDM
        1. MDM Growth and Customer Centricity
      2. Business and Operational Drivers of MDM
        1. Improving Customer Experience
        2. Improving Customer Retention and Reducing Attrition Rates
        3. Growing Revenue by Leveraging Customer Relationships
        4. Improving Customer Service Time:Just-in-Time Information Availability
        5. Improving Marketing Effectiveness
        6. Reducing Administrative Process Costs and Inefficiencies
        7. Reducing Information Technology Maintenance Costs
      3. MDM Challenges
        1. Senior Management Commitment and Value Proposition
        2. Customer Centricity and a 360-Degree View of a Customer
        3. Challenges of Selling MDM Inside the Enterprise
        4. Socializing MDM as a Multidimensional Challenge
        5. Technical Challenges of MDM
        6. Implementation Costs and Time-to-Market Concerns
        7. Data Quality, Data Synchronization, and Integration Challenges
        8. Data Visibility, Security, and Regulatory Compliance
        9. Challenges of Global MDM Implementations
      4. References
    3. 3 MDM Applications by Industry
      1. Industry Views of MDM
      2. Commercial Sector
        1. Financial Services, Banking, and Insurance
        2. Telecommunications Industry
        3. Healthcare Services Ecosystem
        4. Hospitality and Gaming Industry
        5. Manufacturing and Software
        6. Pharmaceutical Industry
        7. Shipping and Logistics
        8. Airlines
        9. Retail Sales
      3. Public Sector
        1. Social Services
        2. Law Enforcement, Border Protection, and Intelligence Agencies
      4. References
  9. Part II Architectural Considerations
    1. 4 MDM Architecture Classifications, Concepts, Principles, and Components
      1. Architectural Definition of Master Data Management
      2. Evolution of Master Data Management Architecture
      3. MDM Architectural Philosophy and Key Architecture Principles
        1. Enterprise Architecture Framework: A Brief Introduction
      4. MDM Architecture Viewpoints
        1. Services Architecture View
        2. Architecture Viewpoints of Various MDM Classification Dimensions
        3. Reference Architecture Viewpoint
      5. References
    2. 5 Data Management Concerns of MDM Architecture: Entities, Hierarchies, and Metadata
      1. Data Strategy
        1. Guiding Principles of Information Architecture
        2. Data Governance
        3. Data Stewardship and Ownership
        4. Data Quality
        5. Data Quality Tools and Technologies
      2. Managing Data in the Data Hub
        1. Data Zone Architecture Approach
        2. Operational and Analytical MDM and Data Zones
        3. Loading Data into the Data Hub
        4. Data Synchronization
        5. Overview of Business Rules Engines
        6. Data Delivery and Metadata Concerns
        7. Enterprise Information Integration and Integrated Data Views
      3. References
    3. 6 MDM Services for Entity and Relationships Resolution and Hierarchy Management
      1. Architecting an MDM System for Entity Resolution
        1. Recognizing Individuals, Groups, and Relationships
        2. MDM and Party Data Model
        3. Entity Groupings and Hierarchies
        4. Challenge of Product Identification, Recognition, and Linking
      2. MDM Architecture for Entity Resolution
        1. Key Services and Capabilities for Entity Resolution
        2. Entity Resolution and MDM Reference Architecture
        3. Entity Recognition, Matching, and Generation of Unique Identifiers
        4. Matching and Linking Services and Techniques
      3. Aggregating Entity Information
      4. Data Hub Keys and Life-Cycle Management Services
        1. Key Management and Key Generation Service
        2. Record Locator Services
      5. References
    4. 7 Master Data Modeling
      1. Importance of Data Modeling
      2. Predominant Data Modeling Styles
      3. MDM Data Modeling Requirements
      4. Data Modeling Styles and Their Support for Multidomain MDM
        1. Approach 1: The “Right” Data Model
        2. Approach 2: Metadata Model
        3. Approach 3: Abstract MDM-Star Model
      5. References
  10. Part III Data Security, Privacy, and Regulatory Compliance
    1. 8 Overview of Risk Management for Master Data
      1. Risk Taxonomy
      2. Regulatory Compliance Landscape
        1. Integrated Risk Management: Benefits and Challenges
      3. Regulatory Compliance Requirements and Their Impact on MDM IT Infrastructure
        1. The Sarbanes-Oxley Act
        2. Gramm-Leach-Bliley Act Data Protection Provisions
        3. Other Regulatory/Compliance Requirements
      4. Key Information Security Risks and Regulatory Concerns
        1. Identity Theft
        2. GLBA, FCRA, Privacy, and Opt-Out
      5. Key Technical Implications of Data Security and Privacy Regulations on MDM Architecture
      6. References
    2. 9 Introduction to Information Security and Identity Management
      1. Traditional and Emerging Concerns of Information Security
        1. What Do We Need to Secure?
        2. End-to-End Security Framework
        3. Traditional Security Requirements
        4. Emerging Security Requirements
      2. Overview of Security Technologies
        1. Confidentiality and Integrity
        2. Network and Perimeter Security Technologies
        3. Secure HTTP Protocols/SSL/TLS/WTLS
        4. Application, Data, and User Security
      3. Integrating Authentication and Authorization
        1. SSO Technologies
      4. Web Services Security Concerns
        1. Authentication
        2. Data Integrity and Confidentiality
        3. Attacks
        4. WS-Security Standard
      5. Putting It All Together
      6. References
    3. 10 Protecting Content for Secure Master Data Management
      1. Data Security Evolution
        1. Emerging Information Security Threats
        2. Regulatory Drivers for Data Protection
        3. Risks of Data Compromise
        4. Technical Implications of Data Security Regulations
      2. Data Security Overview
        1. Layered Security Framework
        2. Data-in-Transit Security Considerations
        3. Data-at-Rest Protection
      3. Enterprise Rights Management
        1. ERM Processes and MDM Technical Requirements
        2. ERM Use Case Examples
      4. References
    4. 11 Enterprise Security and Data Visibility in Master Data Management Environments
      1. Access Control Basics
        1. Groups and Roles
        2. Roles-Based Access Control (RBAC)
      2. Policies and Entitlements
        1. Entitlements Taxonomy
        2. Transactional Entitlements
      3. Entitlements and Visibility
        1. Customer Data Integration Visibility Scenario
        2. Policies, Entitlements, and Standards
        3. XACML
      4. Integrating MDM Solutions with Enterprise Information Security
        1. Overview of Key Architecture Components for Policy Decision and Enforcement
        2. Integrated Conceptual Security and Visibility Architecture
      5. References
  11. Part IV Implementing and Governing Master Data Management
    1. 12 Building a Business Case and Roadmap for MDM
      1. Importance of the MDM Business Case and the Current State of the Problem
      2. MDM Sponsorship Scenarios and Their Challenges
        1. Business Strategy–Driven MDM
        2. IT Strategy–Driven MDM
      3. What MDM Stakeholders Want to Know
      4. Business Processes and MDM Drivers
      5. Benefits and Their Estimation
        1. Traditional Methods for Estimation of Business Benefits
        2. Economic Value of Information as MDM Business Case Estimation Technique
      6. Importance of the MDM Roadmap
        1. Basic MDM Costs
        2. MDM Roadmap Views
      7. Conclusion
      8. References
    2. 13 Project Initiation
      1. Implementation Begins
        1. Addressing the Complexity of MDM Projects
      2. Scope Definition
        1. Business Processes
        2. Lines of Business and Functions
        3. Customer Touch Points, Product Types, and Account Types
        4. Levels of Aggregation and Relationship Types
        5. Entities and Attributes
        6. Systems and Applications in Scope
        7. MDM Data Hub Solution Architecture
        8. Data Hub Architecture Styles
        9. Phased Implementation of Customer Data Hub
        10. Artifacts That Should Be Produced in the Project Initiation Phase
      3. Project Work Streams
      4. References
    3. 14 Entity Resolution: Identification, Matching, Aggregation, and Holistic View of the Master Objects
      1. Holistic Entity View and a 360-Degree View of a Customer: Frequently Used Terms and Definitions
        1. Reasons for False Positives in Party Matching
        2. Reasons for False Negatives in Party Matching
      2. Attributes and Attribute Categories Commonly Used for Matching and Identification
        1. Identity Attributes
        2. Discriminating Attributes
      3. Record Qualification Attributes
      4. Customer Identification, Matching Process, and Models
        1. Minimum Data Requirements
        2. Matching Modes
        3. Defining Matching Rules for Customer Records
        4. Effect of Chaining
        5. Break Groups and Performance Considerations
        6. Similarity Libraries and Fuzzy Logic for Attribute Comparisons
      5. Summary of Data-Matching Requirements and Solutions
      6. References
    4. 15 Beyond Party Match: Merge, Split, Party Groups, and Relationships
      1. Merge and Split
        1. Merge
        2. Split
      2. Relationships and Groups
        1. Direct Business Relationships with an Individual
        2. Households and Groups
        3. Relationship Challenges of Institutional Customers and Contacts
        4. Relationship Challenges of Institutional Customers
        5. Additional Considerations for Customer Identifiers
      3. References
    5. 16 Data Synchronization, MDM System Testing, and Other Implementation Concerns
      1. Goals of Data Synchronization
      2. Technology Approach to Use Case Realization
        1. MDM Data Hub with Multiple Points of Entry for Entity Information
        2. Considerations for the Transaction Hub Master Model
        3. Batch Processing
        4. Synchronization and Considerations for Exceptions Processing
      3. Testing Considerations
        1. Testing of MDM Data and Services
        2. Testing MDM Services
        3. Creation and Protection of Test Data
      4. Considerations for the MDM Application and Presentation Layers
        1. Data Hub Management, Configuration, and Administration Applications
        2. Reporting
      5. Additional Technical and Operational Concerns
        1. Environment and Infrastructure Considerations
        2. Deployment
        3. Considerations for the MDM Data Hub Data Model and Services
      6. References
    6. 17 Master Data Governance
      1. Basics of Data Governance
        1. Introduction to and History of Data Governance
        2. Definitions of Data Governance
        3. Data Governance Frameworks, Focus Areas, and Capability Maturity Model
      2. Data Governance for Master Data Management
        1. Data Quality Management
        2. Data Quality Processes
        3. Master Data Governance Policies for Data Quality
        4. Master Data Governance Metrics for Information Quality
        5. The Existing Approaches to Quantifying Data Quality
        6. Information Theory Approach to Data Quality for MDM
        7. The Use of Matching Algorithm Metrics
      3. How to Make Data Governance More Focused and Efficient
        1. Agile Data Governance
        2. Overlaps with Business Requirements Generated by Departments and Business Functions
        3. Overlaps with the Enterprise IT
        4. Data Governance and Data Governance Frameworks
        5. Processes and Metrics
        6. Data Governance Software
        7. Data Governance and Edward Deming Principles
      4. Conclusion
      5. References
  12. Part V Master Data Management: Markets, Trends, and Directions
    1. 18 MDM Vendors and Products Landscape
      1. MDM Market Consolidation
      2. Major MDM Vendors
        1. IBM
        2. Oracle
        3. Informatica
        4. SAP
        5. SAS DataFlux
        6. Tibco
        7. Dun & Bradstreet Purisma
        8. Acxiom
      3. References
    2. 19 Where Do We Go from Here?
      1. Review of the Key Points Covered in the Preceding Chapters
        1. A Brief Summary of Lessons Learned
      2. Main Reasons MDM Projects Fail
        1. Review of the Key Reasons for MDM Project Failure
      3. MDM Guiding Principles
      4. Master Data Management: Trends and Directions
        1. MDM Market Trends
        2. MDM Technical Capabilities Trends
      5. References
  13. Part VI Appendixes
    1. A List of Acronyms
    2. B Glossary
  14. Index
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