Customer Reviews

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

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Danette McGilvray's new book is a welcome addition to the data quality literature. Finding and eliminating root causes of data errors is essential to any data program. And most people "learn quality improvement by doing," following step-by-step instructions--much as someone just learning to cook sticks close to the recipe.

McGilvray does an excellent job of putting quality improvement in context and narrowing her focus. Make no mistake. This book is specially written for project managers, who must lead improvement teams over often-confusing terrain, and for team members who must do the work.

This book is clearly written. It is richly detailed and chock full of templates that will help project teams move rapidly. It gets my heartiest endorsement.

At first when I recieved this book over in The Netherlands (much quicker than estimated by the way!) I thought it would become a little hard to read. This was because of the big size and amount of pages (289) of the book.

But when I looked further into the book it became clear that it was a thoroughly, but very well readable book. The writer has found a way to describe difficult things in an easy and understandable way.

By the way; the writer (Danette MacGilvray) years ago got into the field of Data Quality because she worked on a assignment at Hewlett-Packard. It was this project where worked together with "consultant" Larry English and got inspired and educated by him and his TIQM-method.

By using many bulletpoints, easy steps and sub-steps, examples, check-lists, boxes and templates this book has become easy and fun to read from A-Z. On the other hand, when in you're daily practice you have to deal with a diffucult IQ or Data Quality problem this book comes in als handy, because it is very good for reference-purposes.

The book's own website (http://www.books.elsevier.com/companions/d9780123743695) with lots of material makes this all complete.

With her Ten Steps approach (based on years of experience in the work field of Danette) the writer has found a way to specify and bullet-point the most important data issues in you're company, get the fundings you need and break down difficult data quality projects in 10 steps.

Not only is the book based on her own experience, this book is also a blend of experience and proven techniques from people from the Information or Data Quality field like Larry English (Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits), Jack Olson (Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems), Tom Redman (Data Quality: The Field Guide) and Gwen Thomas.

So by reading this you get the best of all!

I work as a Data proces & quality manager in an home shopping business, so we are a "data intensive organisation". I think I will use this book (and the 10 steps) quite often.

I have read everything Tom Redman and Larry English have written. Their work has been very instructive and has helped me a great deal in my work. In fact, I used their work, as well as that of luminaries like Jack Olsen, to gain approval for an enterprise-wide information quality management program at a Fortune 500 bio-pharmaceutical company. I am now responsible for executing this program and having these responsibilities, there is no reference that I'm finding more useful than Danette McGilvrey's book.

This is not just a book. It is a "How To" manual. Danette's book fills a real gap in the Data Quality literature. If you want to improve your company's data quality management practices through excellence in executing data quality projects, there is nothing else you can read that is quite as practical and hands-on.

This book is a practical approach to data quality. I like that it gives many different dimensions to data quality, so that we can easily drill down into why the "data is wrong" by having a common vocabulary.

It's a practical approach to getting the data clean and keeping it that way. It's written in a very approachable way that doesn't talk down to me as a reader. I am very happy with my purchase

This book was long waited; it contains a complete and easy guide about how to implement a data quality program. We used it to design our IQ Project at the Maimonides University in Argentina and it help us a lot!!! We also use it to prepare our classes and the way the information is presented is very useful both to us and our students.

This well-written, easy to read (and to reference particular topics) book not only provides a comprehensive guide to achieving the required results on a data quality project but also a wealth of ideas and arguments for convincing business sponsors and IT teams of the fact that data cleansing is only one aspect of data quality.

The author provides clear and logical steps, activities and template suggestions that can be applied to build a holistic view of the issues and the development of comprehensive, sustainable data quality solutions.

I investigated many Data Quality methodologies/books over a period of 12 months and this book has remained the only one that I feel provides the most comprehensive, tool agnostic solution and includes aspects of the 10 core Data Management Functions as defined in the DMBOK. This again makes the ideas Danette discusses in the 10 steps and her framework to address Data Quality, not only a roadmap to a practical data quality solution but also provides some steps in establishing the core components for the implementation of a data management program in manageable chunks that in turn can be developed as a useful basis to for an enterprise data management program. As soon as I saw the framework for Information Quality in the introduction, I knew this was a method worth investigating in more detail. This tool alone is valuable for ensuring that all factors are considered before embarking on a project, thus providing more accurate planning information and the ability to manage expectations effectively through identified quick wins. Not many data management initiatives can achieve this.

It is not often that "specialists" are able to put their ideas on paper in a way that is easy to understand and apply after one reading. Danette has provided a wealth of information that is practical and relevant in an easy to read DIY manual!

I would recommend that all data management practitioners should consider having a copy of this book on their desk.

This is an outstanding, clear, succinct, and useful book.

Even in this age, data problems continue to plague us. Additionally, when performing any quantitative analysis, it is essential to assess the soundness of your underlying data.

As a PMP and professional that utilizes Lean-Six-Sigma techniques often, I'm very impressed with this work. The author provides an easy-to-follow, structured approach (DMAIC and Generic) to approaching data and formulating a plan to assess its soundness. Lastly, she provides simplified (yet effective) checklists that outline the quality dimensions/characteristics for any data. These checklists should be in the back of the mind for all professionals, students, researchers, and decision makers that utilize data. Whereas this knowledge was once the purview of software developers, software testers, analysts, researchers, it is now available and easy to understand for general business decision makers.

I highly recommend.

Bill Minckler, MBA, PMP--Production Data Management Division

I acquired this book at a Data Governance and Information Quality Conference last year (2010). This has to be one of the most practical and succinct reference on this highly complex topic. First, the author introduces you to the Framework for Information Quality. Then she guides you through a very comprehensive in-depth 10 step process in order to achieve your goal. Finally, you will map the 10 steps to the Framework so everything makes perfect sense. Throughout the text, you will find ample "Definition" and "Best Practice" highlights from the author's steep field experience and that you will appreciate.

One of the best feature of this book is the author's effective writing style. I really felt the author directly communicating with me which is quite rare among the technical literature out there. And she helps you focus on concepts that explains each step. It is very readable so you won't get caught up in any technical jargon. Furthermore, it would be so appealing to business managers or execs who are thinking of executing a data quality project.

I highly recommend this wonderful reference to any data governance, data quality, data management professional - especially if you are getting started.

As Data Quality Manager at an energy management firm, we are working to move from reactive data cleanup to proactive data quality. From understanding the problem and creating the business case through controls and communication, Danette's book provides an excellent roadmap to ensure that we cover all the necessary steps with the appropriate level of investment. The "10 Steps" framework is so clear and intuitive that I have used it as a frame of reference when I conduct trainings and presentations. I recommend this book for anyone who considers data their most valuable asset and who plays a role in ensuring the quality of that information.

Danette - I purchased your book in late December, at the recommendation of Anne Marie Smith. It was such a great investment, that I feel as though it had returned its value after just one day of reading. In fact, I read the entire book in less than a week. I just could not put the book down. It was like biting into a favorite sandwich. Thank you so very much for making this available for data quality specialists. As president of DAMA - Philadelphia/Delaware Valley Chapter, I will recommend this book to chapter members as well as data quality practitioners.