Practitioner's Guide to Operationalizing Data Governance

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.

Table of Contents:
Acknowledgments xiii 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