A Systematic Literature Review of Supporting Factors for Big Data Analytics (BDA) in Public Sector Auditing
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The application of Big Data Analytics (BDA) in auditing offers significant benefits, including increased accountability and transparency, as well as reduced operational costs. BDA is also expected to improve the quality and reliability of audit results used for decision-making. The role of BDA in public sector auditing is crucial, as it helps detect anomalies or fraud, enhance oversight, and evaluate implemented policies. Despite its benefits, the application of BDA in public sector auditing still faces various challenges that need to be addressed. This study aims to analyze the factors that support the implementation of BDA in public sector auditing and identify the challenges encountered during its implementation. This research uses a systematic literature review (SLR) approach with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study also employs the Content Validity Index (CVI) to validate the relevance of the identified factors and their classification. The results reveal eight factors that support the use of BDA in public sector auditing: perceived organizational benefits; process management; data privacy, security, and governance; data quality; people aspects; auditor aspects; organizational aspects; and systems, tools, and technologies. Public sector auditing needs to consider these factors when implementing BDA to improve audit effectiveness, efficiency, and the quality of oversight. Proper implementation of BDA can strengthen transparency and accountability in public financial management and policy oversight.
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