Data Quality and Business Rules

Abstract

A critical component of improving data quality is being able to distinguish between “good” (i.e., valid) data and “bad” (i.e., invalid) data. The common definition of data quality focuses on the concept of “fitness for use,” yet because data values appear in many contexts, formats, and frameworks, this simple concept can devolve into extremely complicated notions as to what constitutes fitness. The conventional wisdom dictates that in order to improve data quality, we must be able to measure the levels of data quality. Consequently, to be able to measure levels of data quality, we must know what defines a valid value. In this chapter, we explore a framework for defining data quality and business rules that qualify data values within their context, as well as the mechanism for using a rule-based system for measuring conformity to these business rules.