Data Quality: Empowering Businesses with Analytics and AI

by Prashanth Southekal

Discover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI , veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.

304 pages, Hardcover

Published February 1, 2023

Reviews

[Ray · Rating 4 out of 5]

Helpful all-in-one-place book to justify the need for emphasis on and attention to data quality. Data can be a huge benefit for an organization, but it needs to be clean and organized, otherwise, it's useless, wasteful of resources, or even detrimental.

Southekal mentions a variety of topics and concepts related to data and data quality. The book is structed by DARS, define, analyze, realize and sustain, a process of advancing an organizations data quality. Topics include justification for data quality, data science/AI/modelling, ethics, data engineering, a bit of cybersecurity related to data and protecting sensitive data.

Recommended to anyone that works with data, especially those fighting the fight for more attention to data quality. I think it's helpful to learn more of the terminology for those that were not traditionally educated in data science or data engineering.

[Andy · Rating 3 out of 5]

Clear and comprehensive -- almost taxonomic -- overview of the role of data quality in a business context. Helpful appendices summarizing relevant acronyms and listing both technical and soft skills for working on data quality. Helps lower the barrier to entry and understanding for someone wanting to start working in this field, but does not go into great detail on any one topic. It's value lies more in enabling you to identify specific topics of interest for further study.

[Kärt · Rating 4 out of 5]

This book is a good starting point for getting an overview of a variety of data related topics. It doesn’t go into much detail, but you will learn what to look into.