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

About the technical reviewers

Deepak Bhardwaj

Deepak is a seasoned data mesh and MLOps leader, who is adept at designing cutting-edge data platforms. He champions a decentralized approach, fostering autonomous data teams and empowering organizations to unlock their data assets. Currently leading a data mesh-based platform, Deepak collaborates on structure, governance, and lifecycles for enhanced team autonomy. Specializing in MLOps, he optimizes platforms for the entire machine learning lifecycle, emphasizing cloud-native technologies.

Piotr Czarnas

Piotr, recognized as one of the “Top Data Quality voices on LinkedIn,” serves as the CEO of DQOps, a leading data quality monitoring company. With over 20 years of expertise in the field, he is a seasoned professional dedicated to addressing data quality challenges. Piotr’s leadership has been instrumental in steering his company towards excellence, providing robust solutions for monitoring and enhancing data...

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