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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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Toc

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

Conclusion: Embracing a quality-driven data culture

After being introduced to data quality, you saw how critical the quality of data is when building effective AI models, and how you have to make it part of your overall data strategy if your organization is going to succeed in using the latest advancements in AI to drive business value.

But before you can improve your data quality, you need to measure it. You can start doing that today by simply asking your data consumers, “Do you trust your data?” By doing this regularly through surveys, you’ll gain a valuable data point that you can use to measure the quality of the data.

This will inform you about how people feel about your data quality, but you can do more. The next step is to gain greater visibility of exactly how big a data quality problem you have, and where you have it. You can do this cheaply by running data quality checks and performing one-off profiling of your data, or you can invest in a data...

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