Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM)

I have been using Danette's original book the first edition of Executing Data Quality Projects since it came out, as a Director of Data Management at Honeywell, and used it as a reference in my doctoral dissertation for aspects of DQ, and find the book to be a great learning vehicle for staff who worked for me, and helped us to move forward. This second edition is even better with examples, formatting, and I have recommended probably 150 different companies to buy a copy as I educated them on Data Quality. Danette is the quintessential expert in data quality, and if you buy 1 book on data quality, data governance, or data management buy this one. Great Job Danette.

I found Danette's book to be a tremendous resource. We work with large utility companies on data migration, remediation and maintenance programs that start off with very little structure. Implementing these strategies prior to project initiation will not only save money but mitigate scope creep and improve the overall outcomes.I recommend this book for anyone looking to implement a documented and repeatable process into their data management policies and programs.

I've owned the first edition of Danette's book for many many years now. The book is worn out through use, and I often come back to it for practical advice on data quality. Danette's philosophy of focusing on the underlying WHY, while also providing a systematic framework to developing DQ is exactly what organizations need. Further, I've now gotten to know Danette a little from the conference circuit and her wisdom and willingness to share is second to none. Truly a data hero of mine. You should not hesitate to pick up this book!

Most comprehensive guide to DQ. I have referenced this book throughout my career and refer it to my entire team. Your search for DG and DQ book stops here!

While there are many books written around the role of a Chief Data Officer and Data Strategy design, Danette McGilvray does a fantastic job at providing frameworks for implementing data projects. Which is the more hands on, tactical way of thinking, fused with a strategic view.

My overall and very short reaction to this edition of Ms. McGilvray’s book is that it is a “creeping tent”. The author has done an excellent job of expanding the comprehensiveness of the first edition of the book, which, by doing so, has led her to include material on subjects that are not about data quality at all. Data quality is still “the long pole in the tent” of the book, but the scope of the book has “creeped” outward to encompass many data management and project management topics that are not naturally within the purview of “data quality” – although they are part of what you’d expect in a work about “/Executing/ Data Quality /Projects/”.As a guide for doing something, Ms McGilvray took on the always-difficult (and often impossible) task of trying to explain how do something in a practical step-by-step way that can be followed by a practitioner while simultaneously keeping it general enough to be applicable in wide variety of situations. I think she has generally succeeded in balancing the two in this book.There are many details of the work that I would quibble about or that I simply disagree with. For example, I disagree with her use of “data and information” throughout the book – sometimes they’re synonymous, sometimes they’re not.Conversely, there is much I strongly agree with. For example, I appreciate her continual emphasis on the importance of data specifications and definitions and agree with what she has to say about these. I also agree that when it comes to data quality improvement, preventing errors takes precedence over fixing errors.I was a big fan of and frequently recommended the first edition of this book. While second edition has all the right pieces - I can’t think of anything she’s left out - and all the pieces are well organized and well explained, I feel like there may be too many pieces that dilute the primary focus.All in all, I would still recommend this work for it’s breadth and comprehensiveness.

"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.

While I have been using Danette's 2008 Executing Data Quality Projects book as a framework for more than five years, I’ve always found myself dipping in and out of it. 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. Templates have also been overhauled, and there are now more than twenty-five of them. My three favorites are: 'Data Specification Evaluation Template,' 'Information Anecdotes Template,' and the 'Price Tag of Poor-Quality Data Template.'

If you’re serious about data quality, then this is an essential, practical reference: densely packed with workable prescriptions from one of the leading experts: how to organise data quality expectations; real-world methods for root-cause analysis and much more. There are plenty of books on data quality, ranging from accessible (even light-hearted) calls to action, to the authoritative but dry DMBoK, but this is the one you want by your side in the trenches.