Big Data Analytics and Auditing - Implementation and knowledge

Abstract: Purpose: The purpose of the thesis is to increase the understanding of phenomena surrounding the implementation of Big Data Analytics into the audit methodology, within the context of medium and large-sized audit firms, and how auditing knowledge and its dissemination affects the implementation process. Theoretical perspectives: An analytical model based on previous research regarding Big Data Analytics and Auditing, The Audit Profession, Legitimacy Theory in the context of the implementation of new technology, Audit Knowledge and Knowledge sharing. Methodology: An iterative qualitative thesis, where a literature review was conducted to scope the field, find areas of interest and gaps to cover. Lacking research covering Big Data Analytics in the context of auditing was discovered and an area of interest decided. Semi-structured interviews were conducted to capture and analyse practitioners perceived notions regarding the implementation of Big Data Analytics into the audit methodology. Empirical foundation: 11 interviews were conducted with 13 people within the audit profession, with roles including senior analytics, certified auditors, and associates, see Appendix 3 for full disclosure. Conclusions: The implementation of Big Data Analytics into the audit methodology is perceived to enable improvements in the form of increased audit quality and efficiency, albeit these opportunities are dependent on the profession’s ability to handle the inherent risks and issues associated, where this thesis has identified perceived risks including expanded expectation gap, deprofessionalisation, and two knowledge gaps.