Book Image

Data Quality in the Age of AI

By : Andrew Jones
Book Image

Data Quality in the Age of AI

By: Andrew Jones

Overview of this book

As organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.

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Table of Contents (13 chapters)
Free Chapter
1
Data Quality in the Age of AI
Data Quality in the Age of AI
2
Executive summary
Executive summary
Target audience
3
Understanding data quality
Understanding data quality
Defining data quality
Assessing data quality
4
Unlocking AI’s potential with data
Unlocking AI’s potential with data
High cost of poor data quality
Measuring the quality of your data
5
Improving data quality at the source
Improving data quality at the source
Incentivizing data producers
Decentralizing your data: Quality by design
6
Case studies: Positive impact of data quality
Case studies: Positive impact of data quality
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7
Cultivating a data culture that values quality
Cultivating a data culture that values quality
Adopting a product mindset
Prioritizing quality over quantity
Assigning roles and responsibilities
Embedding data governance
8
Conclusion: Embracing a quality-driven data culture
Conclusion: Embracing a quality-driven data culture
9
About the author
About the author
10
About the technical reviewers
About the technical reviewers
11
Additional reading
Additional reading
Why subscribe?
12
Other Books You May Enjoy
Other Books You May Enjoy
Packt is searching for authors like you
13
Bibliography
Bibliography

Additional reading

You can learn more about data quality issues from the following sources:

  • Askham, Nicola. “Navigating Data Mesh and Evolving Data Governance: A Practical Guide.” NikolaAksham.com, November 30, 2023. https://www.nicolaaskham.com/blog/2023/11/30/navigating-data-mesh-and-evolving-data-governance-a-practical-guide. This guide discusses how data governance is evolving alongside emerging architecture patterns such as data mesh.
  • Colsey, Jack. “‘The dashboard looks broken!’: How should data teams respond to incidents?” Incident.io, November 21, 2023. https://incident.io/blog/incident-management-for-data-teams. It discusses how data teams can start following a mature incident response process.
  • Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media, 2022. The seminal book on data mesh by the person who coined it, discusses each of the principles in detail, including data products and...
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