- Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach
- Contains real examples from around the world, gleaned from the author’s consulting practice and from those who implemented based on her training courses and the earlier edition of the book
- Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices
- A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
Anyone responsible for the quality of data and information. Individual contributors and practitioners, along with managers of those doing the data quality work, project or program managers, and internal or external consultants. Practitioners: data analysts, data stewards, business analysts, subject-matter experts, developers, programmers, business process or data modelers/designers, database administrators
Table of Contents
1. Data Quality and the Data-Dependent World
2. Data Quality in Action
3. Key Concepts
4. The Ten Steps Process
5. Structuring Your Project
6. Other Techniques and Tools
7. A Few Final Words
Appendix: Quick References
- No. of pages: 376
- Language: English
- Copyright: © Academic Press 2021
- Published: May 21, 2021
- Imprint: Academic Press
- eBook ISBN: 9780128180167
- Paperback ISBN: 9780128180150
About the Author
Affiliations and Expertise
Ratings and Reviews
(Total rating for all reviews)
Thiago R. Sun Jun 05 2022
Through this book we can learn practical aspects of managing data quality and how to apply to real world scenarios! It's a must have for everyone dealing with this kind of projects and for data governance professionals.
David F. Thu May 05 2022
One of a kind book for those serious about data quality improvement initiatives
I have been using Danette's 2008 Executing DQ Projects book as a framework for more than five years. It's a book you keep on your desk rather than on your shelf. When a particular challenge arises, I return to specific sections for inspiration. Though many readers will end up consuming the book in a similar practical fashion, I decided to read it through cover-to-cover when the second edition was published in 2021. I’ve found the update a worthy one. Enhancements include a greater focus on human interactions, callout boxes with over a decade of real-world examples, and extensive testimonials for the method in action. There are now more that 25 templates covering topics like 'Data Specification Evaluation', 'Information Anecdotes' and the 'Price Tag of Poor-Quality Data'. If you need to find a way to come unstuck from making progress in DQ projects - you won't be disappointed with this book.
Nicole J. Thu Apr 28 2022
Great for Data Quality teams and Strategy consultants
"Executing Data Quality Projects" was recently updated in 2021. In the book, McGilvray discusses the latest best practices for improving an organization's Data Quality. She includes examples, several templates, and practical advice for executing a successful initiative. The "Ten Steps" refers to a systematic approach that combines a conceptual framework to understand Data Quality with the necessary tools and techniques to improve it. The book makes use of real world projects to highlight how these principles work to enhance Data Quality. McGilvray emphasizes never addressing Data Quality for its own sake, but instead as a way to advance the organization's specific mission. The Ten Steps methodology can be scaled up and down and applied to many Data Quality related situations. I found this book useful when choosing the next best action for my team based on our organization's data maturity and related goals.
Colin M. Thu Oct 07 2021
A great resource that's become even better!
I am a great fan of Danette's original "Executing Data Quality Projects" book and was expecting the second book to be great as well, however, I was pleasantly surprised when my book arrived. Tho book offers great examples that one can follow and is also extensive in terms of its coverage from a depth and breadth perspective, not only on Data Quality but in supporting Data Management disciplines. I highly recommend the book!