Enhancing the implementation of data analytics by internal auditors

Abstract

Internal auditors provide various value-adding services to organizations and generally use sample auditing when performing internal audit engagements, which poses data sampling risks due to the inherent possibility that the selected sample will not be a true reflection of organizations’ complete datasets. This can be overcome by the use of data analytics, enabling the testing of organizations’ entire datasets. Recent studies, however, have reported that the uptake of data analytics by internal auditors is relatively slow. The main objective of this study is to attend to the slow uptake of data analytics by internal auditors. First, the study alludes to how data analytics can be used by internal auditors when performing internal audit engagements. Second, the study adds to previous studies reporting on data analytics adoption factors by proposing a sociotechnical data analytics implementation framework, which could be used by internal auditors when implementing data analytics. The first part of the framework contains the key elements that should be considered by internal auditors when implementing data analytics whilst the second part of the framework sets out the data analytics implementation steps. The sociotechnical perspective of the proposed framework increases the likelihood of the successful implementation while reaping the full benefits associated with the implementation of data analytics. The study could entice internal auditors and serve as guidance to internal auditors and internal audit functions in the implementation of data analytics as part of internal audit engagements, providing a point of departure on the data analytics implementation journey.