The application of big data and analytics to auditing is sparking a lot of interest in both research and practice. Along with other technological developments, big data and analytics are expected to drive the digitization of auditing and to improving its effectivity and efficiency. While several use cases and first literature reviews on this topic have already been published, the categories for classifying the use cases are still fragmented. By employing a systematic taxonomy development process, we developed a taxonomy that draws upon conceptual work and use cases from the academic literature. This
taxonomy provides dimensions and characteristics that help to classify use cases for big data in analytics in auditing in a structured manner.