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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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Chapter 4 Quantification of the Impact of Data Quality1

4.0 Introduction

The key drivers for ensuring data quality in business processes are well known. There are many regulatory, legal, and contractual implications in working with data that is not fit for its intended purpose, and the literature (Redman [1998] and Haug et al. [2011]) suggests that in a large corporation the impact of poor data quality can range between 8 percent and 12 percent of revenue, with an average being 10 percent. Therefore, it is important to design and develop a methodology to quantify the impact of poor-quality data to enable us to understand the factors impacting the data quality and take suitable actions. In this chapter, we describe a framework that can be used to quantify the impact of poor-quality data. This framework is useful in the Define and Assess phases of the DAIC approach.

4.1 Building a Data Quality Cost Quantification Framework

In designing a methodology to quantify the impact of data quality, it is important to understand the paths that data uses to travel throughout an organization by answering the following questions:

  • Where (and from whom) is the data element received or created?
  • What is the process that it goes through? What are the transfers and transformations?
  • How many people touch the process, and what are the systems it goes through?

Answers to these questions are critical to understanding whether a given critical data element (CDE) has a negative impact in more than ...

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