In this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You'll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM.
Table of Contents:
Foreword xiii
Preface xvii
Acknowledgments xxi
Introduction 1
Part I: Planning Your Customer MDM Initiative 7
Chapter 1: Defining Your MDM Scope and Approach 9
MDM Approaches and Architectures 9
Analytical MDM 11
Operational MDM 14
Enterprise MDM 18
Defining the Business Case 20
Cost Reduction 21
Risk Management 22
Revenue Growth 23
Selecting the Right MDM Approach 23
Data Management Maturity Level 24
Addressing the ROI Question 27
Summary 27
Note 28
Chapter 2: Establishing Effective Ownership 29
The Question of Data Ownership 29
Executive Involvement 31
MDM with Segmented Business Practices 31
A Top-Down and Bottom-Up Approach 32
Creating Collaborative Partnerships 33
Can Your Current IT and Business Model
Effectively Support MDM? 33
The Acceptance Factor 34
Business Access to Data 35
Coordination of MDM Roles and Responsibilities 36
Summary 38
Notes 38
Chapter 3: Priming the MDM Engine 39
Introduction 39
Positioning MDM Tools 40
Data Integration and Synchronization 42
Data Profiling 43
Data Migration 46
Data Consolidation and Segmentation 55
Reference Data 57
Metadata 60
Summary 63
Notes 63
Part II: The Implementation Fundamentals 65
Chapter 4: Data Governance 67
Initiating a Customer Data Governance Model 67
Planning and Design 69
Establishing the Charter 70
Policies, Standards, and Controls 78
Implementation 85
Process Readiness 85
Implement 88
Maintain and Improve 91
Summary 93
Notes 94
Chapter 5: Data Stewardship 95
From Concept to Practice 95
People 96
MDM Process Core Team 97
Operational Process Areas 102
Processes 107
Data Caretaking 108
Summary 109
Chapter 6: Data Quality Management 111
Implementing a Data Quality Model 111
A Process for Data Quality 114
Drivers 115
Data Quality (DQ) Forum 117
Controls/Data Governance 119
Data Analysts 120
Design Team 123
IT Support/Data Stewards 125
Metrics 126
Establishing a Data Quality Baseline 127
Context 127
Data Quality Dimensions 129
Entities and Attributes 129
Putting It All Together 132
Data Alignment and Fitness Assessment 136
Data Correction Initiatives 137
Summary 140
Note 140
Chapter 7: Data Access Management 141
Creating the Business Discipline 141
Beyond the System Administrator 142
Creating the Right Gatekeeper Model 144
Preparing 145
Employee Data 146
Access Management Requirements 146
Add User Group Names 148
Map Privileges to Requirement Categories 149
Profiling the Data 150
Implementing and Managing the Process 152
Testing and Launching the Process 157
Resolve Issues Immediately 157
Auditing and Monitoring 158
Segregation of Duty (SoD) Management 159
Summary 161
Notes 161
Part III: Achieving a Steady State 163
Chapter 8: Data Maintenance and Metrics 165
Data Maintenance 165
Specify, Profile, and Analyze 167
Improve 167
Data Quality Metrics 184
Monitors 185
Scorecards 187
Summary 189
Note 190
Chapter 9: Maturing Your MDM Model 191
How to Recognize and Gauge Maturity? 191
Data Governance Maturity 193
Data Stewardship Maturity 194
Data Quality Maturity 195
Data Access Management Maturity 197
Summary 198
Notes 199
Part IV: Advanced Practices 201
Chapter 10: Creating the Customer 360 View 203
Introduction 203
Hierarchy Management (HM) 206
Operational versus Analytical Hierarchies 207
Single versus Multiple Hierarchies 208
Number of Levels in the Customer Hierarchy 209
Virtual versus Physical Customer Records 211
Legal versus Non-Legal Hierarchies 212
The Elusive, yet Achievable, 360 Customer View 213
Summary 213
Chapter 11: Surviving Organizational Change 215
How Adaptable is Your Customer Master Data? 215
Data Quality Factors 216
Data Completeness 217
Data Consistency 217
Data Integrity 218
The Change Management Challenge 219
Data Governance Can Greatly Assist a Transitioning State 220
Leveraging the Data Stewards and Analysts 220
Adopting Best Practices 222
Summary 222
Chapter 12: Beyond Customer MDM 225
The Leading and Lagging Ends 225
Technology’s Influence on MDM 226
Overcoming the IT and Business Constraints 228
Achieving an Effective Enterprise-Wide MDM Model 230
Where Does MDM Lead? 233
Summary 235
Note 236
Recommended Reading 237
About the Authors 239
Index 241
About the Author :
DALTON CERVO is Senior Solutions Consultant at DataFlux Corporation, assisting customers with master data management, data governance, and data quality implementations. Prior to joining DataFlux, Dalton was a senior program manager at both Sun Microsystems and Oracle, leading the data quality efforts as a member of the data governance team responsible for defining policies and procedures governing the oversight of master customer data. He is an expert panelist and a featured blogger for Data Quality PRO and a contributing author in The Next Wave of Technologies: Opportunities in Chaos (Wiley).
MARK ALLEN is a Senior Consultant and Enterprise Data Governance Lead at WellPoint, Inc. Prior to joining WellPoint, Mark was a senior program manager in customer operations groups at both Sun Microsystems and Oracle. Mark has led Sun's Customer Data Governance Board and has been a member of customer advisory boards for DataFlux, Oracle, and Dun & Bradstreet, where he was a presenter and panel member for various data governance and master data management events and forums.
Please visit their website at www.mdm-in-practice.com.
