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

Chapter 1 The Importance of Data Quality

1.0 Introduction

In this introductory chapter, we discuss the importance of data quality (DQ), understanding DQ implications, and the requirements for managing the DQ function. This chapter also sets the stage for the discussions in the other chapters of this book that focus on the building and execution of the DQ program. At the end, this chapter provides a guide to this book, with descriptions of the chapters and how they interrelate.

1.1 Understanding the Implications of Data Quality

Dr. Genichi Taguchi, who was a world-renowned quality engineering expert from Japan, emphasized and established the relationship between poor quality and overall loss. Dr. Taguchi (1987) used a quality loss function (QLF) to measure the loss associated with quality characteristics or parameters. The QLF describes the losses that a system suffers from an adjustable characteristic. According to the QLF, the loss increases as the characteristic y (such as thickness or strength) gets further from the target value (m). In other words, there is a loss associated if the quality characteristic diverges from the target. Taguchi regards this loss as a loss to society, and somebody must pay for this loss. The results of such losses include system breakdowns, company failures, company bankruptcies, and so forth. In this context, everything is considered part of society (customers, organizations, government, etc.).

Figure 1.1 shows how the loss arising from varying ...