Data Quality Metadata and Decision Making

Abstract

Data quality metadata (QM) is the set of quality measurements associated with the data. Literature has demonstrated that the provision of QM can improve decision performance. In this paper, we examine how information systems, specifically, decision support systems can be designed to help users make better use of QM, using a two-stage approach. In stage-1, we develop a theoretical model and validate it using experimental settings to understand how QM affects decision performance, particularly, the cognitive overload QM creates. In stage-2, based on data visualization literature, we posit that the cognitive load may be reduced by visualization. We develop a visual interface for visualizing data and associated QM. We investigate whether the visual interface will permit a superior integration of QM when compared with a textual interface, even for complex tasks with less-experience users. The results of our experiment largely supported our theory and hypotheses.