Applying the CRISP‐DM Framework for Teaching Business Analytics

• Published in: Decision sciences journal of innovative education Vol. 18; no. 4; pp. 612 - 634
• Main Authors: Jaggia, Sanjiv, Kelly, Alison, Lertwachara, Kevin, Chen, Leida
• Format: Journal Article
• Language: English
• Published: Atlanta Wiley 01.10.2020; Blackwell Publishing Ltd
• Subjects: analytics project; Business Administration Education; Business analytics; CRISP‐DM; Data Analysis; Data Collection; data wrangling; Experiential Learning; Higher education; Information Retrieval; Pedagogy; Problem Solving; Programming Languages; Story Telling; storytelling; Teaching; World Problems
• ISSN: 1540-4595; 1540-4609
• DOI: 10.1111/dsji.12222

ABSTRACT Experiential learning opportunities have been proven effective in teaching applied and complex subjects such as business analytics. Current business analytics pedagogy tends to focus heavily on the modeling phase with students often lacking a comprehensive understanding of the entire analytics process including dealing with real‐life data that are not necessarily "clean" and/or small. Similarly, the emphasis on analytical rigor often comes at the expense of storytelling, which is among the most important aspects of business analytics. In this article, we demonstrate how the philosophy of the Cross Industry Standard Process for Data Mining (CRISP‐DM) framework can be infused into the teaching of business analytics through a term‐long project that simulates the real‐world analytics process. The project focuses on problem formulation, data wrangling, modeling, performance evaluation, and storytelling, using real data and the programming language R for illustration. We also discuss the pedagogical theories and techniques involved in the application of the CRISP‐DM framework. Finally, we document how the CRISP‐DM framework has proved to be effective in helping students navigate through complex analytics issues by offering a structured approach to solving real‐world problems.