ABSTRACT
This chapter describes some data science and process engineering approaches as they relate to quality concepts in detail for the purpose of facilitating a discussion of integrated approaches. A good data quality (DQ) program satisfies various requirements that ensure that the data are fit for the intended purpose and are of a high quality. This requires a disciplined DQ program that can be applied across the organization. The typical DQ program needs to be focused on building and institutionalizing processes that drive business value and promote a good impact on society. The chapter describes a structured DQ approach composed of four phases designed to solve DQ problems or issues. This approach is based on the phases of Define, Assess, Improve, and Control (DAIC) and so it is sometimes referred to as the data quality problem-solving approach. The comprehensive approach is aimed at building the best practices and processes for DQ measurement and improvement.
