Executing Data Quality Projects

Executing Data Quality Projects

Ten Steps to Quality Data and Trusted Information (TM)

2nd Edition - May 21, 2021

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  • Author: Danette McGilvray
  • eBook ISBN: 9780128180167
  • Paperback ISBN: 9780128180150

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Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.

Key Features

  • 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

Product details

  • 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

Danette McGilvray

Danette McGilvray has devoted more than 25 years to helping people around the world enhance the value of the information assets on which their organizations depend. Focusing on bottom-line results, she helps them manage the quality of their most important data, so the resulting information can be trusted and used with confidence—a necessity in today’s data-dependent world. Her company, Granite Falls Consulting, excels in bridging the gap between an organization’s strategies, goals, issues, and opportunities and the practical steps necessary to ensure the “right-level” quality of the data and information needed to provide products and services to their customers. They specialize in data quality management to support key business processes, such as analytics, supply chain management, and operational excellence. Communication, change management, and human factors are also emphasized because they affect the trust in and use of data and information. Granite Falls’ “teach-a-person-how-to-fish” approach helps organizations meet their business objectives while enhancing skills and knowledge that can be used to benefit the organization for years to come. Client needs are met through a combination of consulting, training, one-on-one mentoring, and executive workshops, tailored to fit any situation where data is a component. Danette first shared her extensive experience in her 2008 book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann), which has become a classic in the data quality field. Her Ten Steps™ methodology is a structured yet flexible approach to creating, assessing, improving, and sustaining data quality. It can be applied to any type of organization (for profit, government, education, healthcare, non-profit, etc.), and regardless of country, culture, or language. Her book is used as a textbook in university graduate programs. The Chinese translation was the first data quality book available in that language. The 2021 second edition (Elsevier/Academic Press) updates how-to details, examples, and templates, while keeping the basic Ten Steps, which have held the test of time. With her holistic view of data and information quality, she truly believes that data quality can save the world. She hopes that this edition can help a new generation of data professionals, in addition to inspiring those who already care about or have been responsible for data and information over the years. You can reach Danette at danette@gfalls.com. Connect with her on LinkedIn and follow her on Twitter at Danette_McG. To see how Granite Falls can help on your journey to quality data and trusted information, and for free downloads of key ideas and tem¬plates from the book, see www.gfalls.com.

Affiliations and Expertise

President and Principle, Granite Falls Consulting, Inc., UT, USA

Ratings and Reviews

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  • Thiago R. Sun Jun 05 2022

    Great Book!

    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!