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