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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

Incentivizing data producers

For data producers to take on the responsibility of providing better quality data—and to do it well—they need to have incentives that align with their work. There are many ways to do this. Every organization that has grown beyond a start-up needs a way of incentivizing multiple teams to work together to build something of value for the business.

One effective approach is a top-down strategy. By aligning strategic objectives that rely on the creation and utilization of high-quality data, organizations can optimize their structure to facilitate collaboration among relevant teams. Additionally, employing key performance indicators (KPIs) and other prioritization methods can ensure that teams are held responsible for their contributions toward achieving this goal.

For example, if you’re tracking data incidents, you could create KPIs around the number of incidents, their severity, and how common the root causes are. While you will...

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