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Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM) Paperback – July 25, 2008

ISBN-13: 978-0123743695 ISBN-10: 0123743699 Edition: 1st

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Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM) + Data Quality Assessment + The Practitioner's Guide to Data Quality Improvement (The Morgan Kaufmann Series on Business Intelligence)
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Product Details

  • Paperback: 352 pages
  • Publisher: Morgan Kaufmann; 1 edition (July 25, 2008)
  • Language: English
  • ISBN-10: 0123743699
  • ISBN-13: 978-0123743695
  • Product Dimensions: 10.8 x 8.4 x 0.9 inches
  • Shipping Weight: 2.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.9 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #790,437 in Books (See Top 100 in Books)

Editorial Reviews


My esteemed colleague describes a practical approach for planning and managing information quality. I recommend you read, understand, and apply the learnings found here.
- Larry P. English, President and Principal, Information Impact International, creator of the TIQM Quality System. Conceiver and co-Founder of the International Association for Information and Data Quality

In a subject that is long on talk and short on practical advice for implementation, Danette McGilvray is a refreshing exception. If you want to know HOW to execute data quality projects, read this book -- everything you need to know is in here.
- David Plotkin, Data Quality Manager, California State Automobile Association

This book is a gem. Tested, validated and polished over a distinguished career as a practitioner and consultant, Danette's Ten Steps methodology shines as a unique and much needed contribution to the information quality discipline. This practical and insightful book will quickly become the reference of choice for all those leading or participating in information quality improvement projects. Experienced project managers will use it to update and deepen their knowledge, new ones will use it as a roadmap to quickly become effective. Managers in organizations that have embraced generic improvement methodologies such as six sigma, lean or have developed internal ones would be wise to hand this book to their Black Belts and other improvement leaders.
- C. Lwanga Yonke, Information Quality Practitioner.

Danette's book takes a pragmatic and practical approach to achieving the desired state of data quality within an organization. It is a "must-read" for any organization starting out on the road to data quality.
- Susan Stewart Goubeaux, Director, Business Intelligence, FHLBanks Office of Finance

"Data quality" has become one of those hackneyed phrases in our industry that everyone supports, but only a few organizations have achieved to the degree they need to move forward in their industries. What is required is a guide to explain to the business people who want better data just how to get it. This book is just such a guide. While the individual steps should not be a great surprise, her organization makes them immediately actionable to a degree previous books have not. In short, this is definitely required reading for anyone embarking on a data quality project.
- David Hay, President, Essential Strategies

Danette has taken what has previously been presented in the abstract and made an excellent, concrete guide toward improving data quality.
- John Ladley, President of IMCue Solutions

Using this methodology, you will never lose your way on your data quality project! This book is peppered with tips, guidelines, templates, cross-references, and call-out icons. Plus, there are many easy-to-follow examples for the most common types of data quality projects.
- Larissa T. Moss, President, Method Focus Inc.

This book presents a valuable reference for not just data professionals, but also project managers and business representatives interested in or responsible for establishing, maintaining, and/or improving data and information quality. What sets this book apart from others in the field is the business impact-driven approach to assessing and improving data quality, and the specific steps and techniques it provides every step of the way.
- Mehmet Orun, Senior Manager / Principal Architect, Data Services CoE, Fortune 250 Company

"Comprehensive" is the first word I would use to describe this book. It addresses so many nuances of every aspect of data quality assessment and improvement--things that would go unmentioned by more superficial treatments. Bravo!
- Michael Scofield, Manager, Data Asset Development, ESRI, Inc.

This book is a "must-own" for business and technical data quality managers and practitioners. Danette clearly demonstrates where her process will add value to quality projects that stand-alone or as the backbone of a successful data integration effort.
- Robert S. Seiner, KIK Consulting & Educational Services, LLC, The Data Administration Newsletter, LLC

Danette's writing style is appropriate for her audience, the content is superb, and her Ten Steps approach is clear, easy to follow but comprehensive. This is an excellent book and I would think it will be an essential reference for any effort in data quality.
- Anne Marie Smith, PhD., Director of Education and Principal Consultant, EWSolutions, Inc.

Danette has compiled a valuable toolkit for managing information quality improvement projects. Her clear, concise definitions of concepts also make it a nice primer on the principles of information quality for data professionals, business managers, or students. I would recommend this practical handbook to anyone embarking on an information quality project.
- Eva Smith, MSIM, CCP, CDMP, Instructor, Computer Information Systems

No two data quality projects are the same. Some are large efforts focused entirely on improving some quality aspect of information. Others are subprojects within other efforts, such as a data migration. Still others are led by a few individuals trying to make a difference as they perform their everyday activities. What I like about McGilvray's Ten Steps approach is that it can serve any of these situations. This book provides a structured, easy-to-understand, and easy-to-govern methodology that you can apply to the degree that is appropriate for you.
- Gwen Thomas, President, The Data Governance Institute

About the Author

Danette McGilvray is president and principle of Granite Falls Consulting, Inc., a firm specializing in information and data quality management to support key business processes around customer satisfaction, decision support, supply chain management, and operational excellence.

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Customer Reviews

4.9 out of 5 stars
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See all 22 customer reviews
This book is clearly written.
T. C. Redman
As an Enterprise DQ Operations Manager, "Executing Data Quality Projects" is a must that details the "how to" methodology to execute data remediation projects.
Daniel Bucosky
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.
Noeleen Clements

Most Helpful Customer Reviews

11 of 11 people found the following review helpful By T. C. Redman on September 2, 2008
Format: Paperback
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.
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10 of 11 people found the following review helpful By ABL Hijlkema on July 25, 2008
Format: Paperback
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 ([...]) 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 (
...Read more ›
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6 of 6 people found the following review helpful By Andrew Wynn on October 23, 2008
Format: Paperback
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.
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2 of 3 people found the following review helpful By Anne Purcell on July 28, 2008
Format: Paperback
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
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Format: Paperback
... I am referring to the popular "For Dummies" series, which, to me, is characterized by accessibility, a practical focus, and an affinity for lists. On the last item particularly, "10 steps" delivers in spades; this is truly project manager's approach to data-quality improvement. Viewed in the context of data-quality literature, I am not sure how much of the material is new, and some lists are less valuable than others - but accessibility, practicality and concreteness (again, achieved by applying project manager's attitude to the data-quality challenge) score big time.
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Format: Paperback
As one of the previous reviewers remarked, data quality is one of those subjects that is long on talk, and short on practicality.

Enter Danette McGilvray and her utterly practical approach to implementing data quality as a discipline and a way of life in your company. I can say this with complete conviction, because I have used Danette's approach to gently instill some data discipline, data quality and data governance in a multi-national corporate environment where "data" was a four-lettered word in all the wrong ways, and where associating yourself with it in any way was usually and rapidly followed by the modern equivalent of a public hanging.

The book itself is clearly and engagingly written, with enough examples, suggestions, templates and whatnot to tickle even the most jaded data fancy, and if that's not enough, there's always the extra goodies on her website - [...] Like the website, the book is light enough to dip into and out of when you choose, but by no means light on content. Above all, it is practical! You really can lift information straight from its pages and apply them in your world - no need to set up heavy bureaucratic structures, complex processes, or reams of templates before you begin thinking about how to make it all work. The title is a bit of a misnomer in this regard - the ten steps don't require you to complete one before progressing to the next; they are more like ten stepping stones in a ring around you, each of which enables visible progress towards data quality and better data governance.

Danette's simple message of "begin where you are, and take it from there" is the best advice that anyone contemplating a data quality initiative will ever receive, and her book is the only one they will ever need.
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