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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 3 The DAIC Approach1

3.0 Introduction

As discussed, DQ programs contribute to building capability with the help of four important work streams to successfully manage and implement data quality. In Chapter 2, we discussed the first two work streams that are related to DQ governance structure. In this chapter, we discuss the remaining two work streams that are focused on the portfolio of DQ projects and the monitoring and control aspects with the Define, Improve, Analyze, and Control (DAIC) approach. This comprehensive approach helps us understand the current state of data quality, organize around information critical to the enterprise and the business, and implementation practices and processes for data quality measurement and improvement.

There are many standard project methodologies, such as Six Sigma approaches (like DMAIC and DMADV), that can be leveraged to create a DQ project approach to ensure good project execution. Before going into the details of the proposed DQ approach, let us briefly discuss Six Sigma methodologies.

3.1 Six Sigma Methodologies

The Six Sigma is a business process methodology that allows companies to drastically improve their operational performance by designing and monitoring their business activities in ways that minimize waste and resource use while maximizing customer satisfaction through a collection of managerial, engineering, design, and statistical concepts. As described in Jugulum and Samuel (2008), Motorola first employed Six Sigma ...

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