Missing Data in OLAP Cubes

Abstract

Online analytical processing (OLAP) engines display aggregated data to help business analysts compare data, observe trends, and make decisions. Issues of data quality and, in particular, issues with missing data impact the quality of the information. Key decision-makers who rely on these data typically make decisions based on what they assume to be all the available data. The authors investigate three approaches to dealing with missing data: 1) ignore missing data, 2) show missing data explicitly (e.g., as unknown data values), and 3) design mitigation algorithms for missing data (e.g., allocate missing data into known value categories). The authors evaluate the approach with focus groups and controlled experiments. When one tries to inform decision-makers using the approaches in the research, the authors find that they often alter their decisions and adjust their decision confidence: individual differences of tolerance for ambiguity and pre-existing omission bias in the decision context influence their decisions.