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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 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

Assigning roles and responsibilities

It’s only by being clear about roles and responsibilities that diverse groups of people can work together effectively and efficiently to realize the goal of extracting the most business value from data. Let’s define the roles of the data generator and the data consumer.

Data consumers

Often, people only think of data consumers as a data practitioner, for example, a data engineer, a BI analyst, or a data scientist. The primary tasks of these professionals require them to consume and work with data, and as such, they are highly reliant on the quality and reliability of that data. But they are not the only data consumers in your organization.

While data consumers cannot be responsible for the quality of the data they use, they do play a major role in shaping that data. They need to be able to articulate their requirements to the data producers and demonstrate the value they can generate through the application of data. They...

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