8 - Dimensions of Data Quality

Abstract

This chapter focuses on the practical process of aligning business user data quality expectations that can be measured and then correlated to the identified business impacts. Various ways to help classify data quality expectations with ways to measure conformance to these expectations are elaborated in the chapter. These metrics are used to quantify the levels of data quality and are used to identify the gaps and opportunities for data quality improvement across different applications within the enterprise. Instead of relating the issue of poor data quality using anecdotes, the chapter arms the practitioner with guidelines for evaluating stability and predictability associated with quality measurements and distinguishing between common causes and special causes of data failures. Measuring fitness of data for any specific purpose based on the characteristics and needs of the business users helps resolving data quality issues based on an objective assessment of an organization's level of data quality maturity with respect to the dimensions of data quality. The basis for assessing the quality of data is to create a framework that can be used for asserting expectations, providing a means for quantification, establishing performance objectives, and applying the oversight process to ensure that the participants conform to the policies. This framework is based on dimensions of data quality with any others that are specifically relevant within the industry, organization, or even just between the IT department and its business clients.