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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 14 Building a Data Quality Practices Center

14.0 Introduction

Thus far, we have discussed building an effective DQ program and the methods, frameworks, and approaches that are essential for executing the program. In order to build these capabilities throughout a company, it is necessary to establish a strong DQ practices center (DQPC). The DQPC will have the operational capability to provide services, tools, and governance to deliver tangible insights and business value from the data. In this chapter, we describe the building of such a center with the required capabilities.

14.1 Building a DQPC

The target state objective for any data quality program should be to create a fully functional data quality practices center (DQPC). The DQPC will have the required capabilities and will offer services to assist various business functions and business units. The fundamental building blocks identified for the DQPC are shown in Figure 14.1, and they should be kept in mind throughout the evolution of the DQ program.

As shown in Figure 14.1, the DQPC will have four important components: enablers, governance, monitoring and improvements, and analytical insights. A brief description of each component is provided as follows.

  1. Enablers: This component is responsible for delivering DQ services such as standardizing the DQ toolset, supporting metadata, and conducting various DQ assessment activities, besides providing required DQ resources and expertise to accelerate DQ adoption.

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