Practitioner's Guide to Operationalizing Data Governance
SAS Institute Inc
1. Auflage Mai 2023
240 Seiten, Hardcover
Wiley & Sons Ltd
Weitere Versionen
Discover what does--and doesn't--work when designing and building a data governance program
In A Practitioner's Guide to Operationalizing Data Governance, veteran SAS and data management expert Mary Anne Hopper walks readers through the planning, design, operationalization, and maintenance of an effective data governance program. She explores the most common challenges organizations face during and after program development and offers sound, hands-on advice to meet tackle those problems head-on.
Ideal for companies trying to resolve a wide variety of issues around data governance, this book:
* Offers a straightforward starting point for companies just beginning to think about data governance
* Provides solutions when company employees and leaders don't--for whatever reason--trust the data the company has
* Suggests proven strategies for getting a data governance program that's gone off the rails back on track
Complete with visual examples based in real-world case studies, A Practitioner's Guide to Operationalizing Data Governance will earn a place in the libraries of information technology executives and managers, data professionals, and project managers seeking a one-stop resource to help them deliver practical data governance solutions.
Chapter 1 Introduction 1
Intended Audience 2
Experience 2
Common Challenge Themes 4
Chapter 2 Rethinking Data Governance 17
Results You Can Expect with Common Approaches to Data Governance 18
What Does Work 21
Rethinking Data Governance Summary 23
Chapter 3 Data Governance and Data Management 25
Results You Can Expect Focusing Purely on Data Governance or Data Management 26
SAS Data Management Framework 26
Aligning Data Governance and Data Management Outcomes 38
Misaligning Data Governance and Data Management 43
Data Governance and Data Management Summary 45
Chapter 4 Priorities 47
Results You Can Expect Using the Most Common Approaches to Prioritization 48
A Disciplined Approach to Priorities 50
Utilizing the Model 55
Priorities Summary 64
Chapter 5 Common Starting Points 65
Results You Can Expect with Too Many Entry Points 66
Building a Data Portfolio 66
Metadata 67
Data Quality 70
Data Profiling 75
Common Starting Points Summary 76
Chapter 6 Data Governance Planning 77
Results You Can Expect Without Planning 78
Defining Objectives 78
Defining Guiding Principles 85
Data Governance Planning Summary 88
Chapter 7 Organizational Framework 91
Results You Can Expect When There Is No Defined Organizational Structure 92
Organizational Framework Roles 92
Defining a Framework 94
Aligning the Model to Existing Structures 97
Aligning the Framework to the Culture 100
Simplifying the Model 103
Defining the Right Data Stewardship Model 104
Organizational Framework Summary 109
Chapter 8 Roles and Responsibilities 111
Results You Can Expect When Roles and
Responsibilities Are Not Clearly Defined 112
Aligning Actions and Decisions to Program Objectives 112
Using a RACI Model 119
Defining Roles and Responsibilities 126
Data Governance Steering Committee 126
Data Management 131
Naming Names 131
Roles and Responsibilities Summary 135
Chapter 9 Operating Procedures 137
Results You Can Expect Without Operating Procedures 138
Operating Procedures 138
Workflows 146
Operating Procedures Summary 152
Chapter 10 Communication 153
Results You Can Expect Without Communication 154
Communication Plan Components 154
Sample Communication Plan 156
Communication Summary 160
Chapter 11 Measurement 161
Results You Can Expect Without Measurement 162
What Measurements to Define 162
Program Scorecard - A Starting Point 166
Program Scorecard Sample 172
Measurement Summary 173
Chapter 12 Roadmap 175
Results You Can Expect Without a Roadmap 176
First Step in Defining a Roadmap:
Implementing Your Framework 176
Defining a Roadmap 178
Formality First or Save It for Later? 184
Critical Success Factors 185
Roadmap Summary 188
Chapter 13 Policies 189
Results You Can Expect Without Policies 190
Breaking Down a Policy 190
Contents of a Policy 192
Policy Example - Metadata 193
Policy Example - Data Quality 200
Policy Summary 204
Chapter 14 Data Governance Maturity 207
Results You Can Expect With Maturity 208
Data Governance Maturity Cycle 209
Maturing Your Program 215
Summary 216
About the Author 217
Glossary of Terms 219
Index 221
