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The Practitioner's Guide to Data Quality Improvement (The Morgan Kaufmann Series on Business Intelligence) Kindle Edition

12 customer reviews

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Length: 432 pages

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Editorial Reviews


"There is NOTHING like this out there that I am aware of, and certainly nothing from anyone with same stature as David Loshin."

-David Plotkin, Wells Fargo Bank

From the Back Cover

Business problems are directly related to missed data quality expectations. Flawed information production processes introduce risks preventing the successful achievement of critical business objectives. However, these flaws are mitigated through data quality management and control: controlling the quality of the information production process from beginning to end to ensure that any imperfections are identified early, prioritized, and remediated before material impacts can be incurred. The Practitioner's Guide to Data Quality Improvement shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. This book shares templates and processes for business impact analysis, defining data quality metrics, inspection and monitoring, remediation, and using data quality tools. Never shying away from the difficult topics or subjects, this is the seminal book that offers advice on how to actually get the job done.

Product Details

  • File Size: 1447 KB
  • Print Length: 432 pages
  • Publisher: Morgan Kaufmann; 1 edition (November 22, 2010)
  • Publication Date: November 22, 2010
  • Sold by: Amazon Digital Services, Inc.
  • Language: English
  • ASIN: B004HD63OS
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Lending: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Amazon Best Sellers Rank: #725,048 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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More About the Author

David Loshin, president of Knowledge Integrity, Inc, (, is a recognized thought leader and expert consultant in the areas of data quality, master data management, and business intelligence. David is a prolific author regarding BI best practices, via the expert channel at and numerous books and papers on BI and data quality. His book, "Business Intelligence: The Savvy Manager's Guide" (June 2003) has been hailed as a resource allowing readers to "gain an understanding of business intelligence, business management disciplines, data warehousing, and how all of the pieces work together." His book, "Master Data Management," has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at

David can be reached at

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Most Helpful Customer Reviews

10 of 10 people found the following review helpful By Aceto TOP 1000 REVIEWERVINE VOICE on January 17, 2011
Format: Paperback Vine Customer Review of Free Product ( What's this? )
Mr. Loshin has produced a hands-on, practical work on data quality improvement and management. If you need some theory but a nuts and bolts focus with a framework laid out for you, this is the book for you. You can do a thorough assessment of the quality of your data across several dimensions, and develop a roadmap for making a program of specific improvements.

Transactional data is distinguished here from informational data, especially from what quality means. They differ in nature and in function. Informational data is often mishandled by trying to apply the same standards and principles as you would for transactional data. Because transactional data is primary and in full view of your business operations, it can overshadow informational data. While quality is necessary and vital for transactional data, it is not sufficient for an optimally profitable process. You may be in a line of business where the race is won on the competitive advantage gotten from complex, accurate and flexible informational data. Quality flaws here are not always obvious up front.

I use this book to:

- develop action plans at all levels from assessments to strategy to hard dollar reporting.
- teach myself, my staff and my colleagues (each needing different education)

On the technical level, this book is certainly not the last word; but it is detailed enough and thorough enough to get real work done while organizing your research and development efforts to plan an enterprise level program of data quality improvement both transactional and informational. I have been able to define a stream of benefits at each stage of the program.
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5 of 5 people found the following review helpful By Data Guy on November 16, 2010
Format: Paperback
David Loshin's new book, The Practitioner's Guide to Data Quality Improvement, is well-organized, helpful, and on topic. One of my pet peeves is the poor state of data quality rampant just about everywhere these days... and Loshin's text offers expert guidance on how organizations can remedy that situation.

The book provides a comprehensive look at data quality from both a business and IT perspective. It does not just cover technology issues, but discusses people, process, and technology. And that is important, because this is the mix that is needed in order to initiate any type of quality improvement regimen.

In the book, Loshin shows how to institute and run a data quality program, from start to finish. And this is all helpful information. But I think my favorite chapter of the book is the one on Data Quality Service Level Agreements. This is so because data quality is not a project that can be started and completed. It needs to become an on-going component of our everyday procedures. And only through adopting a service level agreement mentality when it comes to data quality can we ever hope to make data quality monitoring and improvement an accepted, regular component of what we do.
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3 of 3 people found the following review helpful By Erik Gfesser VINE VOICE on March 5, 2011
Format: Paperback Vine Customer Review of Free Product ( What's this? )
Some of the other reviews that have been posted here provide some interesting observations from perspectives that are not always centered on data architecture or general enterprise architecture, and the hope of this reviewer is that he will be able to offer feedback to others on this text based on his consulting experience in these areas. In his preface, David Loshin comments that "this book is intended to provide the fundamentals for developing the enterprise data quality program, and is intended to guide both the manager and the practitioner in establishing operational data quality control throughout an organization, with particular focus on the ability to build a business case for instituting a data quality program", "the assessment of levels of data quality maturity", "the guidelines and techniques for evaluating data quality and identifying metrics related to the achievement of business objectives", "the techniques for measuring, reporting, and taking action based on these metrics", and "the policies and processes used in exploiting data quality tools and technologies for data quality improvement".

With these goals in mind, this reviewer thinks Loshin succeeded in this effort. Taking into account the fact that data quality is an enormous practice area, and success requires understanding of both data and the business to succeed, this introductory text walks the reader step-by-step through a considerable number of topics over which many authors would likely stumble. Some of the explanations that Loshin provides, such as the one in the chapter entitled "Developing a Business Case and a Data Quality Road Map" on how data flaws can incur business impacts, are extremely well done, especially when married with effective diagrams.
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Format: Paperback Vine Customer Review of Free Product ( What's this? )
This book will certainly be helpful if your company is responsible for data quality service level agreements. The Practitioner's Guide to Data Quality Improvement is especially written for managers in organizations that offer data quality service level agreements (DQ SLA).

While the emphasis is on practical approaches, it is clear that most organizations will continue to patch data quality issues as they emerge. The methods advocated by David Loshin will be adopted where senior management holds data quality as a top priority.

What percentage of organizations with data quality issues will have a well-supported Data Quality Oversight Board, Data Coordination Council, Data Quality Audit Team and Data Quality Advisory Board? Of course, as data becomes a greater and greater asset, this will be more common.

The Practitioner's Guide excels in the area of data governance. Data governance (DQ) is a loaded term that encompasses people, processes and technology to handle the data of an enterprise. The DQ scorecard measures against standards outlined in the DQ SLA (see above) and individuals are held responsible for the results.

This book has extra wide margins. I would have traded that for additional line-spacing to enhance readability. The diagrams and tables are helpful. I would have liked critical information about the related professional organizations such as The Data Governance and Stewardship Community of Practice and the Data Governance Conferences.

I recommend this book because it offers a comprehensive practitioner's approach for entities where senior management supports data quality initiatives. For other situations, the book provides a framework for promoting (selling) data quality initiatives.
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