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.

Over the past decade, more and more business schools are attempting to teach business processes (BPs) by using enterprise resource planning (ERP) software in their curricula. Currently, most studies involving ERP software in the academy have concentrated on learning and teaching via self-assessment surveys or curriculum integration. This research extended previous studies by attempting to measure student knowledge acquisition of BP concepts (BPC) about two common BP cycles through hands-on exercises using ERP software. Assessments of students' knowledge about BP were conducted at multiple time points during the study. In addition, a Technology Acceptance Model (TAM)-based survey was employed to analyze student self-assessment about improved understanding of BPs though ERP hands-on exercises. Results from our empirical study indicated that there is no clear evidence that students' knowledge about BPs significantly improved after experiencing ERP software. However, students' self-assessment showed that there is a positive relationship between their comprehension of BPs and hands-on experience with ERP software. Our research findings concurred with previous research, and studies undertaken in other disciplines.

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Unilateral enterprise resource planning (ERP) curriculum improvements from the instructor's perspective are likely to generate only limited success. Understanding student motivations and beliefs with ERP systems is the missing link to effective ERP education. Relatively little attention in the ERP literature has been given to student learning associated with ERP experience, and almost none to factors influencing current and expected student beliefs and behaviors relative to ERP. The complexity of ERP systems demands that beliefs and behaviors be considered when planning ERP curricula. In the present study, the Theory of Planned Behavior was extended to examine students' intentions to explore additional aspects of ERP after their class exercises. When considering all students, attitude and subjective norm had positive and significant effects on intentions to continue ERP learning. Subjective norm also affected attitude, and availability of support materials had a positive effect on subjective norm. Distinctive patterns are found for the construct relationships between student groups who valued ERP education (the engaged) and those who placed little or no value on ERP education (the undecided). Results from competing model analyses indicate that support materials influenced the engaged and undecided groups differently. Strategies for ERP curriculum design are provided.

Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of the following ANN parameters: weights, learning rates, threshold functions, and transformation functions. The spreadsheet ANN model project is given early in the semester, just after ANN is introduced. Students can see effects of ANN parameters as they make changes to spreadsheet model inputs, greatly enhancing discussion of ANN processes. After working with the spreadsheet model, students have expressed an appreciation for decisions based on patterns of historic data, and they like the ability to peek “behind the curtain” at processes of predictive software packages.