Preparing students for careers using business analytics and data-driven decision making
Erland Hejn Nielsen () and
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Erland Hejn Nielsen: Department of Economics and Business Economics, Aarhus University, Postal: Department of Economics and Business Economics, Aarhus University, Fuglesangs Alle 4, DK-8210 Aarhus V
Economics Working Papers from Department of Economics and Business Economics, Aarhus University
Data analytics and performance measurement and management (PM&M) now seem to be deeply rooted disciplines for both professional decision makers and in the business environments. Research articles and consulting companies (e.g., AACSB, 2014) stress the importance of recruiting students with a proficiency in business analytics and of preparing students with knowledge, skills, and ability in the area of business analytics (BA) and machine learning, as these skills will help businesses process data, find patterns and relations, and make decisions and predictions. However, several ideas from BA actually go back to Anthony and Harvard Business School in 1965 and to Tukey and Princeton University in 1962, respectively. The purpose of this paper is first to discuss and show the use of BA for performance management models and decisions. Second, we discuss the content of PM&M and all the uncertainty that surrounds it. Third, we show how to combine BA and PM&M in a bachelor course, and finally we discuss the assumptions and skills necessary for students in relation to completing such a course. In this sense, the nature of our paper is inspirational. Finally, the paper reports the result from a survey made among the students who have taken the course, that is, that students’ interest in data-driven performance is best activated through a combination of hands-on learning and inspirational datasets.
Keywords: Performance measurement and management; quantitative models; business analytics; Monte Carlo simulation; data-driven decisions; system dynamics; algorithms; flow and stock (search for similar items in EconPapers)
JEL-codes: A22 C81 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:aarhec:2020-08
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