Planning for Low End Analytics Disruptions in Business School Curricula

Abstract

Analytics is getting a great deal of attention in both industrial and academic venues. Organizations of all types are becoming more serious about transforming data from a variety of sources into insight, and analytics is the key to that transformation. Academic institutions are rapidly responding to the demand for analytics talent, with hundreds of offerings aimed at producing a broad range of analytical graduates from data scientists to data‐savvy managers and functional specialists. Curricula generally provides best practice methods of tackling descriptive, predictive, and prescriptive analytics; but there has been little discussion about the disruptive nature of increasingly robust analytical tools in the academic space. The net effect of astounding tool capability is empowerment of less technically trained people to address analytical complexities heretofore only comprehensible to data scientists with in‐depth knowledge of mathematics, programming, and statistics. This article examines skills needed for analytics in industry, academic response, and evolving analytic needs through the lens of disruption theories of Clayton Christensen. We offer a direction of curriculum development by supplementing the disruption theory with three interactive components of problem space mapping: processes, tools, and techniques. The challenge for academicians is a dynamic, adaptable curricula addressing multiple levels of data analyses.

Exploratory Analysis to Identify Concepts, Skills, Knowledge, and Tools to Educate Business Analytics Practitioners

“…The results of the peer university analysis as shown in Figure demonstrate that programs in our sample have a focus on the basic analytics skills (e.g., predictive modeling) and the application of the skills in the financial and marketing sectors. However, the results also identify that there is less focus on the managerial and organizational aspects such as project management, which was highlighted as an essential skill in a previous study (Rienzo & Chen, ).…”

“…Wilder and Ozgur (2015) propose seven major courses and implementation guidelines for undergraduate programs. After analyzing seventy programs and four hundred job announcements, Rienzo and Chen (2018) recommend flexible, adaptable, and dynamic curricula.…”

“…There are many software tools and products available to visualize, mine, and model large datasets. Thus, a “one‐size‐fits‐all” analytics curriculum that lacks flexibility and has not been tailored to industry needs will not be able to equip students with tools necessary to succeed in the job market (Rienzo & Chen, ).…”

Analytics Curriculum for Undergraduate and Graduate Students

“…The sources included but are not limited to reports and papers from highly recognized and respected sources such as the Harvard Business Review and the McKinsey Global Institute, including Manyika et al. (2011), IDC (), and Jain (), and include previous studies regarding skills associated with analytics and quantitative related positions (e.g., Liberatore & Luo, ; Rienzo & Chen, ) as well as a review of more established programs at recognized schools (see Table 2A for a list of programs/schools). The schools are generally recognized as having some of the top (and among the first) analytics programs in various ranking.…”

Creating Impactful Student Learning in a Business Analytics Program through Leveraging Scholar‐Practitioner Engagement

“…These partnerships provide a foundation from which academic institutions can facilitate student experiential learning (Sawhney, Maleki, Wilck, & Hashemian, 2016). Experiential learning may be particularly critical for analytics programs given the increasing number of and enhancements to tools, techniques, and processes (Rienzo & Chen, 2018). Others have highlighted the importance of applied practice—specifically applying technical concepts and techniques in a specific business/industry domain (Asamoah, Sharda, Zadeh, & Kalgotra, 2017), knitting together detailed knowledge of data and processes with analytics techniques.…”