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

Danette McGilvray (Author)

Book Description

Publication Date: July 25, 2008 | ISBN-10: 0123743699 | ISBN-13: 978-0123743695 | Edition: 1

In today's world of instant global communication and rapidly changing trends, up-to-date and reliable information is essential to effective competition. Recent 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.

In Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, Danette McGilvray presents a systematic, proven approach to improving and creating data and information quality within the enterprise. She describes a methodology that combines a conceptual framework for understanding information quality with the tools, techniques, and instructions for improving and creating information quality. Her trademarked "Ten Steps" approach applies to all types of data and to all types of organizations.Data quality problems cost businesses billions of dollars each year in unnecessary printing, postage, and staffing costs, in the steady erosion of an organization's credibility among customers and suppliers, and the inability to make sound decisions.

Danette McGilvray presents a systematic, proven approach to improving data quality by combining a conceptual framework for understanding information quality with techniques and instructions for improving it. The Ten Step approach applies to all types of data and to all types of organizations.

* Includes numerous templates, detailed examples, and practical advice for executing every step of The Ten Steps approach.
* 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 planning and information-gathering templates featured in the text, quick summaries of key ideas from The Ten Step methodology, and other tools and information that is available online.

Editorial Reviews

Review

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.

Product Details

• Paperback: 352 pages
• Publisher: Morgan Kaufmann; 1 edition (July 25, 2008)
• Language: English
• ISBN-10: 0123743699
• ISBN-13: 978-0123743695
• Product Dimensions: 8.5 x 0.9 x 11 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: #227,268 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

10 of 10 people found the following review helpful

5.0 out of 5 stars Much needed addition September 2, 2008

By T. C. Redman

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.

10 of 11 people found the following review helpful

4.0 out of 5 stars Practical new book on data quality (projects) July 25, 2008

By ABL Hijlkema

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 (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.

6 of 6 people found the following review helpful

5.0 out of 5 stars Excellent book for Data Quality professionals October 23, 2008

By Andrew Wynn

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.