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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 FREE CHAPTER
2. Executive summary 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

Prioritizing quality over quantity

Since Hadoop came along in 2006 and significantly reduced the cost of storing big data, data engineers have often been focused on how much data they can bring in centrally, with the assumption that they’ll use it to create value later. But by prioritizing quantity over quality, many organizations found it took so much effort to use this data that in practice they just couldn’t justify it. That left them with dark data that was poorly managed and increased the risk of misuse and leaks.

In fact, a report from Seagate found that only 32% of data available to an organization is utilized. That leaves 68% of your data incurring costs, both monetarily and in increased risk, without generating any value.17

It’s the data producers that are responsible for the quality of their data. But the data consumers are still responsible for supporting that investment. Let’s define the roles and responsibilities of both those groups...

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