Measuring Data Quality in Context

Abstract

Maintaining data at a high quality is critical to organizational success. Firms, aware of the consequences of poor data quality, have adopted methodologies and policies for measuring, monitoring, and improving it (Redman, 1996; Eckerson, 2002). Today’s quality measurements are typically driven by physical characteristics of the data (e.g., item counts, time tags, or failure rates) and assume an objective quality standard, disregarding the context in which the data is used. The alternative is to derive quality metrics from data content and evaluate them within specific usage contexts. The former approach is termed as structure-based (or structural), and the latter, content-based (Ballou and Pazer, 2003). In this chapter we propose a novel framework to assess data quality within specific usage contexts and link it to data utility (or utility of data) - a measure of the value contribution associated with data within specific usage contexts. Our utility-driven framework addresses the limitations of structural measurements and offers alternative measurements for evaluating completeness, validity, accuracy, and currency, as well as a single measure that aggregates these data quality dimensions.