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Data Quality in the Age of AI

You're reading from   Data Quality in the Age of AI Building a foundation for AI strategy and data culture

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121435
Length 50 pages
Edition 1st Edition
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Author (1):
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Andrew Jones Andrew Jones
Author Profile Icon Andrew Jones
Andrew Jones
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Table of Contents (13) Chapters Close

1. Data Quality in the Age of AI
2. Executive summary FREE CHAPTER 3. Understanding data quality 4. Unlocking AI’s potential with data 5. Improving data quality at the source 6. Case studies: Positive impact of data quality 7. Cultivating a data culture that values quality 8. Conclusion: Embracing a quality-driven data culture
9. About the author
10. About the technical reviewers
11. Additional reading 12. Other Books You May Enjoy 13. Bibliography

Additional reading

You can learn more about data quality issues from the following sources:

  • Askham, Nicola. “Navigating Data Mesh and Evolving Data Governance: A Practical Guide.” NikolaAksham.com, November 30, 2023. https://www.nicolaaskham.com/blog/2023/11/30/navigating-data-mesh-and-evolving-data-governance-a-practical-guide. This guide discusses how data governance is evolving alongside emerging architecture patterns such as data mesh.
  • Colsey, Jack. “‘The dashboard looks broken!’: How should data teams respond to incidents?” Incident.io, November 21, 2023. https://incident.io/blog/incident-management-for-data-teams. It discusses how data teams can start following a mature incident response process.
  • Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media, 2022. The seminal book on data mesh by the person who coined it, discusses each of the principles in detail, including data products and...
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