An Introduction to the Standard Data Analytics Process for Accounting Students

The aim of this paper is to contribute to the effort to develop material for enabling the next generation of tech-savvy accountants with business analytic capabilities, and increase their exposure and familiarity with tools such as R. More specifically, we leverage the award-winning tax case of Borthick and Smeal (2017) to illustrate the teaching of the Cross Industry Standard Process for Data Mining (CRISP-DM) with R. Teaching the implementation of the data analytics process highlights the role and importance of each one of the stages (i.e., business understanding, data understanding, data preparation, modeling, evaluation, and communication). Implementing CRISP-DM in conjunction with R helps students gain a better understanding of data limitations (e.g., missing values, incomplete data, errors in data) and their implications in the context of the case, while providing a readily available record of all steps taken in a data analytics process (i.e., process documentation).