ABSTRACT: Since data analytics enables data exploration and the uncovering of hidden relationships, in this study we use cluster analysis to gain more insight into governmental data and financial reports. This research initiative is performed using the Design Science Research (DSR) methodology, where we develop and apply an appropriate artifact. We apply our artifact to two different datasets: the first uses the U.S. states’ financial statements, and the second utilizes survey results from the Volcker Alliance about states’ budgeting performance. In both applications, we demonstrate how clustering may be used on governmental data to gain new insights about financial statements and budgeting. This study contributes to the literature in two ways: First, the two applications bring advanced data mining techniques into the not-for-profit domain; and second, the results provide guidance for auditors, academics, regulators, and practitioners to use clustering to gain more insights.
Applications of Data Analytics: Cluster Analysis of Not-for-Profit Data
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Zamil S. Alzamil, Deniz Appelbaum, William Glasgall, Miklos A. Vasarhelyi; Applications of Data Analytics: Cluster Analysis of Not-for-Profit Data. Journal of Information Systems 2021; doi: https://doi.org/10.2308/ISYS-2020-025
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