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Data Stewardship in Action

You're reading from   Data Stewardship in Action A roadmap to data value realization and measurable business outcomes

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781837636594
Length 272 pages
Edition 1st Edition
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Author (1):
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Pui Shing Lee Pui Shing Lee
Author Profile Icon Pui Shing Lee
Pui Shing Lee
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Why Data Stewardship and Why Me? FREE CHAPTER
2. Chapter 1: From Business Strategy to Data Strategy to Data Stewardship 3. Chapter 2: How Data Stewardship Can Help Your Organization 4. Chapter 3: Getting Started with the Data Stewardship Program 5. Part 2:How to Become a Data Steward and Shine!
6. Chapter 4: Developing a Comprehensive Data Management Strategy 7. Chapter 5: People, Process, and Technology 8. Chapter 6: Establishing a Data Governance Organization 9. Chapter 7: Data Steward Roles and Responsibilities 10. Chapter 8: Effective Data Stewardship 11. Chapter 9: Supercharge Data Governance and Stewardship with GPT 12. Part 3:What Makes Data Stewardship a Sustainable Success?
13. Chapter 10: Data Stewardship Best Practices 14. Chapter 11: Theory versus Real Life 15. Chapter 12: Case Studies 16. Index 17. Other Books You May Enjoy

Future of AI for data governance

In the digital age, AI is set to revolutionize data governance. As data-driven decision-making grows, AI’s role in managing, protecting, and leveraging data becomes crucial. AI can automate tasks and predict trends, bringing unprecedented efficiency to data governance.

As we look to the future, the impact of AI on data governance is only set to grow.

Here are the trends:

  • The use of AI and machine learning will revolutionize data quality measurement, data discovery, classification, and lineage. This not only enhances data governance but also allows data stewards to focus on strategic tasks.
  • With the increasing volume and variety of data, AI can boost efficiency, accuracy, and scalability, making data governance more manageable.
  • AI’s ability to detect anomalies and inconsistencies will significantly improve data quality.
  • Sophisticated AI models can predict future trends from historical data, empowering organizations...
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