Academic journal article Journal of Information Systems Education

Managing the Innovation Process: Infusing Data Analytics into the Undergraduate Business Curriculum (Lessons Learned and Next Steps)

Academic journal article Journal of Information Systems Education

Managing the Innovation Process: Infusing Data Analytics into the Undergraduate Business Curriculum (Lessons Learned and Next Steps)

Article excerpt


In this article, the author asserts that the Internet of things, the increased categorization and quantification of business records, and the explosion of social media and user generated content has provided a treasure trove of data that can be transformed into information and ultimately into business insight by the application of analytics (Goh and Sun, 2015), i.e., "the scientific process of transforming data into insight for making better decisions" (Informs, 2016). In this emerging big data/analytics era, businesses are increasingly looking to the information systems discipline to holistically combine data, programs, experiments, and algorithms to aid in decision-making and ultimately to gain competitive advantage (Wilder and Osgur, 2015). The two key players in this arena are businesses that will lead the transformation and academic institutions that will supply much of the talent necessary for the transformation (Wixom et al., 2014). As of today, in the information systems academic arena, there has been considerable experimentation in creating courses to provide the requisite students skills, but few integrated curriculums, and no dominant design (Topi, 2016). One way to gain insight on how best to fill these gaps is to compare a "dream" Business Analytic curriculum put forth by an IS scholar against an actual curriculum model developed at our university (Wang, 2015). Similar to Koch and Kayworth (2009), the author has developed a form of case-based research where the researcher is directly involved in the phenomenon being studied (Baskerville and Wood-Harper, 1996).

The author began the curriculum design effort at his university with a situational analysis of what is changing in the emerging data-centric business environment and how this is affecting business demand for analytic talent. This sets the stage for a curriculum audit of existing IS programs and challenges they are facing. The author then presents a proven model of curriculum change that was used, followed by the components of the new Data Analytics major. Next, the author presents the innovation process model that guided the design of the new major and concludes with how the program compares with an ideal curriculum and what has been learned.


The amount of data in the world has been exploding, and analyzing large data sets--so-called big data--is becoming a critical basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus (Manyika et al., 2011). Organizations around the world are struggling to develop the know-how to aggregate, analyze, and most importantly, monetize the growing surge of available data (Heinz, 2016). The domain of big data has been categorized into two broad but related areas: Data Engineering is the finding, organizing, cleaning, sorting and moving of data; and Data Science is about improving decision making from the ever increasing quantity of data and ultimately generating knowledge and insights by extracting value from data. Because the former, data engineering, is performed at the tactical and operational level, and the latter is done more at the analytical and strategic level, this makes designing a comprehensive data analytics major including both difficult.

A study of the McKinsey Global Institute estimates that by 2018 there will be 4-5 million jobs in the U.S. requiring data analysis skills--and large numbers of positions will only be filled through training or retraining (Data Society, 2016). This new generation of scientists and engineers must possess a broad range of distinctive skills not present in any one academic department. Therefore, the creation of a comprehensive data analytics curriculum must draw upon at least two central areas: computer science (databases and programming) and analytics (math and forecasting). However, data analytic knowledge is quite useful in a variety of discipline areas in the business school, for instance, forensic accounting, digital marketing, application development, financial engineering, and healthcare administration. …

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