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Data Governance Handbook

You're reading from   Data Governance Handbook A practical approach to building trust in data

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803240725
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Wendy S. Batchelder Wendy S. Batchelder
Author Profile Icon Wendy S. Batchelder
Wendy S. Batchelder
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Designing the Path to Trusted Data
2. Chapter 1: What Is Data Governance? FREE CHAPTER 3. Chapter 2: How to Build a Coalition of Advocates 4. Chapter 3: Building a High-Performing Team 5. Chapter 4: Baseline Your Organization 6. Chapter 5: Defining Success and Aligning on Outcomes 7. Part 2:Data Governance Capabilities Deep Dive
8. Chapter 6: Metadata Management 9. Chapter 7: Technical Metadata and Data Lineage 10. Chapter 8: Data Quality 11. Chapter 9: Data Architecture 12. Chapter 10: Primary Data Management 13. Chapter 11: Data Operations 14. Part 3:Building Trust through Value-Based Delivery
15. Chapter 12: Launch Powerfully 16. Chapter 13: Delivering Quick Wins with Impact 17. Chapter 14: Data Automation for Impact and More Powerful Results 18. Chapter 15: Adoption That Drives Business Success 19. Chapter 16: Delivering Trusted Results with Outcomes That Matter 20. Part 4:Case Study
21. Chapter 17: Case Study – Financial Institution 22. Index 23. Other Books You May Enjoy

Data quality defined

Data Quality is the data governance capability that refers to the degree to which data is accurate, reliable, and fit for its intended purpose in a given context. There are several dimensions by which data quality is assessed; these include completeness, accuracy, timeliness, consistency, and relevance. As mentioned previously, data quality is an essential capability for all organizations. Overall data quality across the company is critical, as is data quality in individual data elements, on key reports, as a part of operations, and for the overall functioning of the business. Data quality is the core of building trust in our information. Next, each data quality dimension is defined, along with a few examples to help contextualize what data quality is and how it may show up in your company. We will move quickly into the core capabilities needed to apply these core dimensions after we ground ourselves on the basics:

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