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Reviewer: Chander Shekher Arora

Did you know that the US has paid $28 million in compensation to the Chinese government for killing three Chinese nationals and wounding 20—because of the erroneous bombing of the Chinese Embassy by the US Air Force in 1999 during its attempt to drive Serbs from Kosovo Not everyone knows that while choosing the Chinese embassy as one of its targets, the CIA had thought it to be an armory for Yugoslav weapons. That target was located using an out-of-date address--a data error, which even the usually effective target review process failed to catch. This was not the only time the US mistargeted a weapon. It happens more frequently than one might hope. Of course, everyone—not only in the US, but the world over—still remembers the US elections of 2000. The importance of poor data quality had never before been demonstrated so publicly. Whether the issues involved the accuracy of various counts and recounts, or the need for intense data scrutiny at all stages of the election chain, or the politics behind the entire counting mechanism is for the election authorities to decide. In any case, this has definitely made people everywhere understand that data quality is not some esoteric computer issue, but one that can affect their daily lives in a profound way. Thus, a sound data quality program should be in place—whether at a national or an organizational level—to prevent such disasters or embarrassments. This is the core theme of Redmans convincing, down-to-earth book. Whether the organization is the national defense establishment, or the divisional management of an enterprise preparing reports for a CEO based on the data obtained from a companys data warehouse, decision makers need to be able to trust the data on which they base decisions. The new economy based on the Web runs on data, and the importance of data is only growing. As the author states in his preface, if information technologies are the engines of the information age, data and information are fuels. Clearly, without the right fuel, the engine will sputter and the organization will not go anywhere. The authors highly professional approach takes into account the importance of this subject. The text is organized logically into eight parts spanning 36 short chapters. The last part is a treat for busy readers: it summarizes the book in a chapter, followed by a reorganized repetition of the “Field Tips,” which are given earlier at the end of each chapter. The titles of the main parts are “Who Cares about Data Quality ” “The Business Case for Data Quality,” “The Heart of the Matter,” “Necessary Background,” “Blocking and Tackling,” “Middle Management Roles and Responsibilities,” and “Why Senior Management Must Lead and What It Must Do.” In addition to the main text, the author has provided a good glossary, a list of references, an index, and a preface. The commentary about the 2000 US election is presented as an illustrative appendix. The real icing on the cake is the “Field Tips.” A total of 71 tips are spread through all of the chapters (and repeated in the last chapter). Three samples below give a taste of the prudence they display: “Field Tip 13.1: First, prevent future errors, then clean up existing errors. Unless circumstances are dire, resist efforts to compromise on this point.” “Field Tip 34.3: Do not apply information technology to a poorly defined information chain. The likely result is frustration with the technology and an information chain that produces poor data faster.“ “Field Tip 10.1: Dont be misled. Finding and correcting errors is non-value-added work. It is difficult, expensive, and time consuming as well. Most importantly, it doesnt work very well.” Another advantage of the book is the availability of the figures and tables from the publishers Web site. This can be of great value to academics, as well as to quality consultants, who can cut and paste the figures directly into their presentations. With its well-organized layout and pragmatic coverage, Redman has written a commendable work on the subject He presents a clear message, that software quality is not enough—data quality is at least as important. I recommend this book strongly, especially to CEOs, CIOs, and all professionals involved with quality systems.