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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

Improving data quality at the source

An Experian report conducted in 2021 found that 95% of business leaders reported a negative impact on their business due to poor quality data.10 This underscores the necessity for proactive measures to improve the quality of the data.

Data quality can only be improved at source. If the data source fails to capture information accurately, rectifying it later becomes futile. Similarly, inaccessible data sources can affect user access. If data is delivered infrequently, its timeliness cannot be retroactively improved. Likewise, if data sets are incomplete at the source, there’s nothing you can do to make them complete later.

You can try to work around some of these data quality issues downstream, typically in your data pipelines. For example, you could impute missing values using averages, the most common values, or machine learning algorithms, but these may be inaccurate, introduce bias, and be expensive to compute...

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