Summary by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida
The authors begin by stating that there is a serious shortage of accountants with data analytics skills while analytics and big data are becoming the most important digital competency for organizations. The purpose of this paper is to describe how business schools can include these skills in their accounting programs, and how practicing accountants can update their own skills in the area of data analytics.
Definition and Importance of Data Analytics in Accounting Education
KPMG defined data analytics as "processes by which insights are extracted from operational, financial, and other forms of electronic data internal and external to the organization." AACSB Standards 9 and A7 describe information technology skills and knowledge as an important area, including skills and knowledge related to data creation, data sharing, data analytics, data mining, data reporting, and storage within and across organizations.
Strategies for Incorporating Data Analytics into the Accounting Curriculum
According to the authors, the initial focus should be on data analytics cases, e.g., cases provided by the EY Academic Resource Center, and cases in auditing textbooks including examples for ACL and IDEA.
PricewaterhouseCoopers' recommended that accounting students acquire skills in the following areas:
Legacy technologies, Microsoft Excel and Access,
Structured and unstructured databases, SQL, MongoDB, Hadoop,
Data visualization, Tablea, SpotFire, Qlikview,
Univariate and multivariate regression, machine learning, and predictive tools,
Programming languages such as Python, Java, or R, and
For graduate programs:
Text mining, HTML scraping,
Solving optimization problems, and
Data analytics internships involving real business problems.
Data analytics could be included in the accounting information systems program, or a separate track could be offered with an analytics emphasis. Some universities are already offering online degrees or certificates in analytics. Of the three types of analytics, i.e., descriptive, predictive, and prescriptive, the current emphasis is on the development of prescriptive analytics, i.e., models that specify optimal behavior.
Some generalist sites that provide resources in this area include:
Simply Statistics - https://simplystatistics.org/
R-bloggers - https://www.r-bloggers.com/
Rutgers University's Accounting Web - http://raw.rutgers.edu/index.html
Revolution Analytics - http://blog.revolutionanalytics.com/statistics/
Note: See the Appelbaum, Kogan, and Vasarhelyi summary below for a brief explanation of many analytic tools and methods. I found Wikipedia to be much more informative than any of the sites listed above, and I provided a number of links to Wikipedia in that summary.
Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. An introduction to data analysis for auditors and accountants. The CPA Journal (February): 32-37. (Summary).
Appelbaum, D., A. Kogan, M. Vasarhelyi and Z. Yan. 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems (25): 29-44. (Summary).
Gregg, A. 2017. Start-ups embrace cryptocurrency to raise needed capital: 'Initial coin offerings' let companies raise money without ceding control. The Washington Post (December 4): A13. (Note).
Martin, J. R. Not dated. What is data mining? Management And Accounting Web. http://maaw.info/DataMining.htm
Roberts-Witt, S. L. 2002. Data mining: What lies beneath? Finding patterns in customer behavior can deliver profitable insights into your business. PC Magazine (November, 19): iBiz 1-6. (Summary).