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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 9 Data Quality Issue Resolution

9.0 Introduction

The issue resolution (IR) process encompasses issue identification, tracking, and the resolution of issues through root-cause analysis. The issues involved can be data quality–related, process quality–related, or technology-related. If we identify the category to which a particular issue belongs, we can resolve the issue with the help of the data quality methodology (as described in Chapter 3) or process quality (Six Sigma approach). This is how we can demonstrate the linkage between data quality and process quality approaches. This linkage further benefits the data quality initiative from a methodology and toolset that increases data quality by improving process quality. In this chapter, we explain the structured linkage between data quality and process quality by understanding the processes related to CDEs and using process engineering and data quality tools and techniques. The linkage process explained in this chapter is quite valuable in the Improve phase of the DAIC approach.

9.1 Description of the Methodology1

As mentioned earlier, a major component of this effort is the issues resolution (IR) process. This process is focused on identifying the sources of data quality issues and working to prevent them by root-cause remediation. By approaching the work from the standpoint of IR, we can see the way some issues cut across the various businesses and units of the business. This suggests that, in many cases, the solution ...

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