Contents
Part I Introduction to Master Data Management
1 Overview of Master Data Management
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)
Other MDM Variants: Products, Organizations, Hierarchies
Challenges of MDM Implementation for Product Domain
Introduction to MDM Classification Dimensions
Key Benefits of Master Data Management
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 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
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
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
3 MDM Applications by Industry
Financial Services, Banking, and Insurance
Hospitality and Gaming Industry
Law Enforcement, Border Protection, and Intelligence Agencies
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
Architecture Viewpoints of Various MDM Classification Dimensions
Reference Architecture Viewpoint
5 Data Management Concerns of MDM Architecture: Entities, Hierarchies, and Metadata
Guiding Principles of Information Architecture
Data Stewardship and Ownership
Data Quality Tools and Technologies
Data Zone Architecture Approach
Operational and Analytical MDM and Data Zones
Loading Data into the Data Hub
Overview of Business Rules Engines
Data Delivery and Metadata Concerns
Enterprise Information Integration and Integrated Data Views
6 MDM Services for Entity and Relationships Resolution and Hierarchy Management
Architecting an MDM System for Entity Resolution
Recognizing Individuals, Groups, and Relationships
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
Predominant Data Modeling Styles
MDM Data Modeling Requirements
Data Modeling Styles and Their Support for Multidomain MDM
Approach 1: The “Right” Data Model
Approach 3: Abstract MDM-Star Model
Part III Data Security, Privacy, and Regulatory Compliance
8 Overview of Risk Management for Master Data
Regulatory Compliance Landscape
Integrated Risk Management: Benefits and Challenges
Regulatory Compliance Requirements and Their Impact on MDM IT Infrastructure
Gramm-Leach-Bliley Act Data Protection Provisions
Other Regulatory/Compliance Requirements
Key Information Security Risks and Regulatory Concerns
GLBA, FCRA, Privacy, and Opt-Out
Key Technical Implications of Data Security and Privacy Regulations on MDM Architecture
9 Introduction to Information Security and Identity Management
Traditional and Emerging Concerns of Information Security
Traditional Security Requirements
Emerging Security Requirements
Overview of Security Technologies
Network and Perimeter Security Technologies
Secure HTTP Protocols/SSL/TLS/WTLS
Application, Data, and User Security
Integrating Authentication and Authorization
Web Services Security Concerns
Data Integrity and Confidentiality
10 Protecting Content for Secure Master Data Management
Emerging Information Security Threats
Regulatory Drivers for Data Protection
Technical Implications of Data Security Regulations
Data-in-Transit Security Considerations
ERM Processes and MDM Technical Requirements
11 Enterprise Security and Data Visibility in Master Data Management Environments
Roles-Based Access Control (RBAC)
Customer Data Integration Visibility Scenario
Policies, Entitlements, and Standards
Integrating MDM Solutions with Enterprise Information Security
Overview of Key Architecture Components for Policy Decision and Enforcement
Integrated Conceptual Security and Visibility Architecture
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
What MDM Stakeholders Want to Know
Business Processes and MDM Drivers
Traditional Methods for Estimation of Business Benefits
Economic Value of Information as MDM Business Case Estimation Technique
Addressing the Complexity of MDM Projects
Lines of Business and Functions
Customer Touch Points, Product Types, and Account Types
Levels of Aggregation and Relationship Types
Systems and Applications in Scope
MDM Data Hub Solution Architecture
Phased Implementation of Customer Data Hub
Artifacts That Should Be Produced in the Project Initiation Phase
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
Record Qualification Attributes
Customer Identification, Matching Process, and Models
Defining Matching Rules for Customer Records
Break Groups and Performance Considerations
Similarity Libraries and Fuzzy Logic for Attribute Comparisons
Summary of Data-Matching Requirements and Solutions
15 Beyond Party Match: Merge, Split, Party Groups, and Relationships
Direct Business Relationships with an Individual
Relationship Challenges of Institutional Customers and Contacts
Relationship Challenges of Institutional Customers
Need for Persistent Match Group Identifiers
Additional Considerations for Customer Identifiers
16 Data Synchronization, MDM System Testing, and Other Implementation Concerns
Technology Approach to Use Case Realization
MDM Data Hub with Multiple Points of Entry for Entity Information
Considerations for the Transaction Hub Master Model
Synchronization and Considerations for Exceptions Processing
Testing of MDM Data and Services
Creation and Protection of Test Data
Considerations for the MDM Application and Presentation Layers
Data Hub Management, Configuration, and Administration Applications
Additional Technical and Operational Concerns
Environment and Infrastructure Considerations
Considerations for the MDM Data Hub Data Model and Services
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
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
Overlaps with Business Requirements Generated by Departments and Business Functions
Overlaps with the Enterprise IT
Data Governance and Data Governance Frameworks
Data Governance and Edward Deming Principles
Part V Master Data Management: Markets, Trends, and Directions
18 MDM Vendors and Products Landscape
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
Master Data Management: Trends and Directions