Conceptual Framework to Apply Exploratory Data Analysis in Audit Practice

• In Event: Research on Emerging Technologies

Abstract

Exploratory data analysis (EDA), originating centuries ago, is a data analysis approach emphasizing on pattern recognition and hypothesis generation from raw data. It is usually used as the first step to explore and understand data. Therefore, EDA is essential in any data analysis task and has been applied in many disciplines such as Geography, Marketing, and Operations Management. However, even though some EDA techniques, such as data visualization and data mining, have been used in some
procedures in auditing, EDA has never been employed in auditing in a systematical way. The study aims at contributing to auditing literature by exploring the applications of EDA in the auditing process. Specifically, it identifies the value of EDA in auditing and
proposes a conceptual framework to demonstrate the potential application areas of EDA
in various audit stages in both internal and external audit cycles, how auditors can apply
various EDA techniques in different audit tasks, and a process for auditors to implement EDA. In addition, this paper also discusses how EDA should be integrated in a continuous auditing setting.

Authors

• Qi Liu, Rutgers University

• Miklos A Vasarhelyi, Rutgers University - Newark