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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

Setting up data quality for success

My best advice for setting up your data quality program is to start small, be specific, be transparent, and scale rapidly. Pick a handful of metrics or a couple of key reports, and show the value of understanding the data, tracing the lineage, and measuring the quality of the data. Remediate issues quickly and visibly and show improvement in the data for these key metrics and/or reports.

Be very clear with your stakeholders, both users and executives alike: the goal is not to create perfect data for the enterprise. The goal is to deploy resources (time, money, people) for the most effective return on those resources. You could spend unlimited time and money remediating every data element, but it would be like trying to plug every hole in every boat in every ocean. Sometimes, you just need to build a better boat. You may run into instances where a report is incredibly low quality. It might be easier to completely recreate the report with different...

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