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Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality by Rajesh Jugulum

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Preface

According to Dr. Genichi Taguchi's quality loss function (QLF), there is an associated loss when a quality characteristic deviates from its target value. The loss function concept can easily be extended to the data quality (DQ) world. If the quality levels associated with the data elements used in various decision-making activities are not at the desired levels (also known as specifications or thresholds), then calculations or decisions made based on this data will not be accurate, resulting in huge losses to the ­organization. The overall loss (referred to as “loss to society” by Dr. Taguchi) includes direct costs, indirect costs, warranty costs, reputation costs, loss due to lost customers, and costs associated with rework and rejection. The results of this loss include system breakdowns, company failures, and company bankruptcies. In this context, everything is considered part of society (­customers, organizations, government, etc.). The effect of poor data ­quality during the global crisis that began in 2007 cannot be ignored because inadequate information technology and data architectures to support the management of risk were considered as one of the key factors.

Because of the adverse impacts that poor-quality data can have, organizations have begun to increase the focus on data quality in business in general, and they are viewing data as a critical resource like others such as people, capital, raw materials, and facilities. Many companies have started to establish ...

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