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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

Unlocking AI’s potential with data

Recent advances in AI and increased accessibility to machine learning models that power them have got many organizations excited about how they can be applied to create more value. In fact, in a recent survey by Dataiku and Databricks, 64% of respondents said that they were “likely” or “very likely” to use generative AI for their business over the next year,2 while a report from Segment found that 92% companies are using AI-driven personalization to drive business growth.3

9/10 companies use AI-driven personalization for growth

However, AI is only as good as the data behind it. No amount of tuning a model will help if the data is of poor quality, which is why the same survey from Dataiku and Databricks identified lack of quality data as the primary obstacle to generating value. An inferior model with superior data will always outperform a superior model with inferior data.

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