Applications of Data Analytics: Cluster Analysis of Not-forProfit Data

Zamil S. Alzamil, Deniz Appelbaum, William Glasgall, Miklos A. Vasarhelyi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)199-221
Number of pages23
JournalJournal of Information Systems
Volume35
Issue number3
DOIs
StatePublished - 1 Sep 2022

Keywords

  • audit analytics
  • data analytics
  • governmental data
  • hierarchical clustering
  • k-means clustering
  • not-for-profit data

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