A Framework for Analyzing the Potential Role of Big Data in Auditing: A Synthesis of the Literature

• In Event: Audit Quality 1

Abstract

The purpose of this paper is to develop a framework for systematically analyzing the potential role of big data in auditing, enabling an understanding to be attained of both the value that it can provide to audit practice and the hurdles that will have to be overcome if auditors are to adopt big data. The framework is developed by synthesizing the large and growing literature on big data to identify those factors that will determine the impact of big data on auditing. When considering the drivers and inhibitors facing big data in auditing, the overall point is that auditors do not have the discretion or the incentive to experiment with big data and find novel applications of it the way that managers or scientists do. Other methodologies that expand the use of data such as continuous auditing, process mining and advanced statistical tools have had only limited interest shown in them by auditors and only in limited circumstances and the same may be true with big data. Part of the reason is the satisficing rather than optimizing objective in auditing, part the fact that even where standards allow the use of such techniques they certainly do not compel it and part because auditors do not feel that they have the skill set to implement them effectively. Overall, if big data is to play a role in auditing then a proper understanding needs to be obtained of the unique characteristics of both auditing and of big data.

Authors

• Michael G. Alles, Rutgers, The State University of New Jersey, Newark

• Glen L. Gray, California State University, Northridge