Toward understanding outcomes associated with data quality improvement

Business analytics is driving the way organizations compete. However, the decisions made as a result of any analytics process are greatly dependent on the quality of the data from which they are based. Scholars suggest several data quality improvement methods, and commercial software is now available that can help improve data quality. Although organizations are motivated to find ways to improve analytic capabilities and using these methods has been shown to improve some measures of data quality, there is little understanding of the tangible and intangible outcomes of employing these methods. In this study, we explore outcomes that arise from a data quality improvement process implementation in an operations management environment. Over a three-year period, we conducted a longitudinal single case study at an organization that maintains a large fleet of aircraft, collecting and analyzing qualitative interviews and observations. The findings suggest outcomes (both positive and cautionary) associated with the implementation of data quality improvement processes. These include increased stakeholder commitment to data quality and business analytics, as well as an over-emphasis on program metrics to the peril of operational outcomes, among others. From the basis of our findings, we construct a research agenda, which includes testable Propositions regarding outcomes of data quality improvement.