Rating: 
Summary: The best book I've ever read on Information Quality
Review: This book takes the reader from understanding and applying principles of Quality Management to a step-by-step guide for implementing a quality improvement program.I liked the organization and writing style of this well-referenced text. I found the diagrams and tables very helpful. In the introduction, the author states that the text is a "concept book, a textbook, a reference book, and a practitioner's guide." At first I didn't believe it. Now that I've finished the text, I believe every word. When I read the "Author's Warranty" (and how many books do you know come with a warranty?), I had an inkling that this would be a good book. I was wrong. This is a GREAT book!
Rating:
Summary: An important and unique work
Review: This is an important and unique work that addresses a big problem: data quality. Why is this a problem? Data warehouses are proliferating at a dizzying rate. Since data warehouses are fed by production databases, many of which are legacy systems, the poor quality of existing data quickly becomes [painfully] apparent. I spent the last half of 2000 bringing data warehouses into production and can attest to this sorry fact. However, the author drives home this point in chapter 1, titled "High Costs of Low-Quality Data" by giving nearly three pages of eye-opening examples from real life. This alone should inspire anyone responsible for data integrity or quality, or who uses data to carefully read this book. The big question is "what is quality"? Specifically, "what is information quality"? Answers to these basic questions are given early in the book, and sets the tone for what follows. The foundation of data quality is carefully built by how the author applies quality principles to information, which segues into a chapter on improving information quality. It quickly becomes obvious that Mr. English is a Deming fan - although I am more in the Juran camp, I like the way that the author places data and information quality into a recognizable framework. Things get interesting in the chapters on assessing data and information quality. The two chapters devoted to this subject are strengthened by the chapter on measuring the costs of non quality. This is a great foundation for a business case for data and information quality improvement, which can be expensive. The rest of the book is a step-by-step approach to getting data quality under control using data reengineering and cleansing; proactive measures for data defect prevention, and how to establish an information quality environment. Although I found every chapter to be both informative and thought provoking, I particularly liked the concept of information stewardship (this goes far in aligning IT and business, and places roles and responsibilities where they belong), and the chapter on implementing a quality improvement environment. This is especially valuable because it clearly outlines the critical success factors and steps needed to get there. Who should read this book? Obviously DBAs, data architects and anyone else responsible for designing and implementing data warehouses. It should also be read by key business process owners because they, after all, own the data (or should) and depend on it as the basis for information. In fact, Mr. English's approach and writing make this book highly accessible to non-technical readers, which is probably the book's most valuable aspect. I personally believe that this book is the best on the subject and strongly recommend it.
Rating:
Summary: An important and unique work
Review: This is an important and unique work that addresses a big problem: data quality. Why is this a problem? Data warehouses are proliferating at a dizzying rate. Since data warehouses are fed by production databases, many of which are legacy systems, the poor quality of existing data quickly becomes [painfully] apparent. I spent the last half of 2000 bringing data warehouses into production and can attest to this sorry fact. However, the author drives home this point in chapter 1, titled "High Costs of Low-Quality Data" by giving nearly three pages of eye-opening examples from real life. This alone should inspire anyone responsible for data integrity or quality, or who uses data to carefully read this book. The big question is "what is quality"? Specifically, "what is information quality"? Answers to these basic questions are given early in the book, and sets the tone for what follows. The foundation of data quality is carefully built by how the author applies quality principles to information, which segues into a chapter on improving information quality. It quickly becomes obvious that Mr. English is a Deming fan - although I am more in the Juran camp, I like the way that the author places data and information quality into a recognizable framework. Things get interesting in the chapters on assessing data and information quality. The two chapters devoted to this subject are strengthened by the chapter on measuring the costs of non quality. This is a great foundation for a business case for data and information quality improvement, which can be expensive. The rest of the book is a step-by-step approach to getting data quality under control using data reengineering and cleansing; proactive measures for data defect prevention, and how to establish an information quality environment. Although I found every chapter to be both informative and thought provoking, I particularly liked the concept of information stewardship (this goes far in aligning IT and business, and places roles and responsibilities where they belong), and the chapter on implementing a quality improvement environment. This is especially valuable because it clearly outlines the critical success factors and steps needed to get there. Who should read this book? Obviously DBAs, data architects and anyone else responsible for designing and implementing data warehouses. It should also be read by key business process owners because they, after all, own the data (or should) and depend on it as the basis for information. In fact, Mr. English's approach and writing make this book highly accessible to non-technical readers, which is probably the book's most valuable aspect. I personally believe that this book is the best on the subject and strongly recommend it.
Rating: 
Summary: Deming for data
Review: While providing some of the traditional quality assessment measures, Larry English provides a Deming 14-points approach to information quality and continuous data quality improvement. For example, instead or rewarding those who find major quality problems, change the culture to provide quality early in the process. His chapter on "assessing data definition" quality is an important step often neglected. For example, some of us are may be using minimal metadata (perhaps federally mandated standards) that are inadequate for true enterprise wide data definition. The examples included in the book (particularly in "High costs of low quality data") are instructive, and show how someone saturated with thinking about quality (like Larry English), views such simple things as getting a fax at a hotel. If you are planning a data warehouse, this book might fit nicely into the "Enterprise Infrastructure Evaluation" phase in Moss and Atre's "Business Intelligence Roadmap" terminology.
I would have liked more specific methods of detecting low quality in the section on information quality assessment. The final third of the book, on establishing the information quality environment, provides good direction, but seems too optimistic. How does a single database analyst change a corporate culture and how does a small warehouse group influence the quality processes of hundreds of diverse data sources? This is a good, thought-provoking book.
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