Data Governance Success: Growing and Sustaining Data Governance

✍ Scribed by Rupa Mahanti

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 237
• Category: Library

✦ Synopsis

While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following:
• Data governance perceptions and challenges • Key considerations when implementing data governance to achieve and sustain success• Strategy and data governance• Different data governance maturity frameworks• Data governance – people and process elements• Data governance metrics
This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects.

✦ Table of Contents

Foreword by Dan Power
Preface
Acknowledgments
About This Book
Contents
About the Author
Acronyms and Abbreviations
List of Figures
List of Tables
1 Data Governance Journey—Introduction
1.1 Data Governance
1.2 Data Management Discipline
1.3 Data and Its Governance
Reference
2 Data Governance Challenges and Dynamics
2.1 Introduction
2.2 Data Governance Failures
2.3 Data Governance—Perceptions and Challenges
2.3.1 Vastness and Complexity of the Data Landscape
2.3.2 Data and Data Governance not Priorities
2.3.3 Individuals Needed on Data Governance are in High Demand
2.3.4 Collaboration Challenges
2.3.5 Data Governance Connotations
2.3.6 Executive Buy-in and Lack of Executive and Organizational Support
2.3.7 Costs and Budgets
2.3.8 Organizational Culture, Politics, and Conflicts
2.3.9 Restrictive Nature of Data Governance
2.3.10 Data Governance Perceptions and Misunderstandings
2.4 Key Factors for Ensuring Data Governance Success
2.4.1 Leadership and Management
2.4.2 Strategy and Execution
2.4.3 Organizational Change Management (OCM)
2.5 Data Governance Program—Do’s, Don’ts, Tips, and Lessons Learnt
2.5.1 Tailor Data Governance to your Organization—“One Size Does not Fit All” Approach
2.5.2 Adopt a Non-invasive and Non-disruptive Approach
2.5.3 Accept That the Data Governance Journey will be Difficult
2.5.4 Establish a Strong Base
2.5.5 Identification of Areas for Proof of Concept (POC)
2.5.6 Get Some Quick and Meaningful Wins
2.5.7 Share Lessons Learnt and Success Stories
2.5.8 Keep It as Clear and Consumable as you Can
2.5.9 Have a Clear Focus and Stay Focused
2.5.10 Have the Right Candidates for the Data Governing Body
2.5.11 Strike the Right Balance Between Opposing Goals
2.5.12 Understand the Data Governance Scope First and Then Form the Data Governance Body
2.5.13 Plan for Longevity
2.5.14 Incentivize Participation
2.5.15 Do a Data Governance Program Health Check
2.5.16 Do not Design Data Governance Without Integrating the Key Decision-making Bodies and Other Data Initiatives in your Organization
2.5.17 Avoid Establishing Big Committees
2.5.18 Do not Take the Tick Box Approach to Satisfy the Regulators
2.6 Concluding Thoughts
References
3 Strategy and Data Governance
3.1 Introduction
3.2 Are You Ready for Data Governance?
3.3 Data Governance Maturity Assessment
3.4 Strategy
3.5 Corporate Strategy, Data Strategy, and Data Governance
3.6 Data Governance Strategy
3.7 Building a Business Case for Data Governance
3.8 Data Governance Roadmap
3.9 Data Governance Pilot
3.9.1 Critical Data Domains and Data Sets
3.9.2 The Data Function or Data Initiative
3.10 Role of the Chief Data Officer in Data Governance
3.11 Concluding Thoughts
References
4 Data Governance Maturity Models
4.1 Introduction
4.2 Data Governance Maturity Models
4.3 Data Governance Maturity Model Metamodel
4.4 Data Governance Maturity Models by Different Industry Practitioners
4.4.1 Kalido
4.4.2 DataFlux
4.4.3 Microsoft
4.4.4 Informatica
4.4.5 Oracle
4.4.6 IBM
4.5 Data Governance Maturity Model Proposal
4.6 Data Governance Maturity Assessment
4.7 Summary
References
5 Data Governance Components and Framework
5.1 Data Governance—People, Process, and Tools and Technology
5.2 Data Governance Framework
5.3 Data Governance Components—Broad Categories
5.4 Data Governance—The People Component
5.4.1 Data Governance Organization Structures, Roles, Responsibilities, and Operating Rhythms
5.4.2 Data Ownership
5.4.3 Data Stewards and Data Stewardship
5.4.4 Data Stewardship Models
5.5 Data Governance—The Process Component
5.5.1 Data Principles
5.5.2 Data Policies
5.5.3 Guidelines
5.5.4 Processes
5.5.5 Rules and Standards
5.6 Data Governance—The Tools and Technology Component
5.7 Data Governance Operating Models
5.7.1 Top Down
5.7.2 Centralized
5.7.3 Decentralized
5.7.4 Hybrid
5.7.5 Federated
5.7.6 Crowdsourced
5.8 Concluding Thoughts
References
6 Data Governance—Metrics
6.1 Data Governance Metrics—Introduction
6.2 Desired Characteristics of Data Governance Metrics
6.3 Data Governance Metrics—Health Check
6.4 Data Governance Metrics—Don’ts
6.5 Data Governance Metrics Identification and Selection
6.6 Data Governance Metrics—Categories and Examples
6.6.1 Quantitative and Qualitative Metrics
6.6.2 Progress, Verification, and Impact/Value Metrics
6.6.3 People, Process, Technology, and Data Metrics
6.6.4 Efficiency, Enablement, and Enforcement Metrics
6.7 Data Governance Metric Documentation
6.8 Dashboard and Scorecards
6.9 Concluding Thoughts
References
7 Data Governance Success—Concluding Thoughts and the Way Forward
7.1 Data Governance—A Success Factor
7.2 Data Governance in a Page
7.3 Evaluating Data Governance—The Journey Ahead
References
Appendix A Glossary of Terms
Appendix B Data Governance—Perceptions Versus Realities
Appendix C Bibliography
Index