The Practitioner's Guide to Data Quality Improvement (The Morgan Kaufmann Series on Business Intelligence) 1st Edition

Having been a regular practitioner and advocate of data quality improvement in a large corporate environment, making headway in this challenging area requires all the support, planning, knowledge, and technique you can muster up. From a planning, knowledge, and technique perspective, this practitioner's guide from David delivers all the guidance and insight you can possibly consume on the subject of data quality improvement. If this book doesn't help you develop and execute a quality improvement game plan in your company, it's probably only because you don't have the internal support you need to proceed. Like David's book on Master Data Management, the references and instructive detail provided in this book are spot on and invaluable for anyone who wants to seriously address data quality and data management problems.

As our clients call for more data quality services to complement other data hygiene services we provide for them I turned to David's book to become better educated in this DQ space. I learned a great deal and encouraged all of our clients that subscribe to our e-newsletter to pick up a copy for themselves. You'll be glad you read David's book, filled with practical advice which helps those with an interest in data quality determine to what extent the introduction of bad data can impact the way your company does business.

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. And in his chapter entitled "Metrics and Performance Improvement", the author provides an explanation on drilling through key performance indicators that this reviewer has not seen elsewhere until this effort, and the presentation is exceedingly well done. Other areas of this text that this reviewer especially appreciates are the chapters entitled "Data Requirements Analysis", "Metadata and Data Standards", and "Inspection, Monitoring, Auditing, and Tracking".This reviewer however would like to make potential readers of this book aware that most of what Loshin provides here is high level walkthroughs and examples of pertinent elements within data quality, rather than practical advice on how to approach much of the lower level work that should be expected to take place on a day-to-day basis. For example, in the chapter entitled "Entity Identity Resolution", the author provides a section on matching algorithms that briefly discusses parsing and standardization, abbreviation expansion, edit distance, phonetic comparison, and n-gramming, which consumes just a few short paragraphs. The author does not explain that there are many more matching algorithms currently in use in industry, that in most cases matching exercises need to take into account multiple rather than single algorithms in isolation, that in the world today internationalization takes an ever more important role when performing matching, and that there is a wide variety of commercial tooling available that needs to be assessed based on the needs of the organization.However, armed with this knowledge the reader is sure to make use of this work by utilizing it while planning and strategizing data quality, as well as making use of it as introductory material to understanding what it might take to pursue efforts that require a higher level of data quality maturity such as master data management (MDM), in which case this reviewer recommends "Enterprise Master Data Management: An SOA Approach to Managing Core Information" by Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, and Dan Wolfson (see my review). In the opinion of this reviewer, what Loshin provides here is best suited for managers looking to piece together all of the steps associated with data quality pursuits as well as get a better handle on how each of the steps are interrelated and whether each is a requirement or just an option, possibly looking to solve some aspects of data quality in an evolutionary, piecemeal fashion rather than revolutionary endeavor.

This book has everything you need to know about putting data quality standards into practice. As other reviewers have noted, it's organized in textbook fashion which actually makes it a great tool for practitioners. There are helpful example templates and flowcharts throughout that can be easily adapted into practice.The author, David Loshin, covers everything from the impacts that bad data makes on an organization to building the data quality program to creating a business case for a data quality program and more. So many of the topics covered in the book are ones that many of us think should be automatic for any organization dealing with large amounts of data. However, many aspects of data quality are often ignored, overlooked, or considered "too much effort" to put into place. Loshin provides a comprehensive overview of these aspects and explains why data standards, metadata, data quality assessment, and auditing (to name a few) are all equally important to data quality.Overall, this is a great book that not only explains the practice of data quality improvement but also provides sample tools, templates and plans for putting it into practice.