Big data processing within a statutory audit; using IDEA, SQL server, Excel

Volume, variety and velocity of data generated by information systems of large organizations are growing exponentially. As a result, the conditions under which audits are performed are radically changing. Sample-based procedures used in traditional auditing are insufficient to provide reasonable assurance for transactional data. This thesis deals with the use of Big Data analytics in statutory audit. Three groups of tools that accounting data are processed with in audit practice are reviewed. Using Microsoft SQL Server (relational database management system), Microsoft Excel (including Power Query and Power Pivot add-ins) and CaseWare IDEA generalized audit software, auditors are able to process vast amounts of data. In the theoretical part, the main objectives of statutory audit, the relevant ISAs (International Standards on Auditing) and the current legislation on auditing in the Czech Republic (Czech Act No. 93/2009 Coll.), Big Data characteristics, benefits, limitations and the impact of audit data analytics on auditor behavior are discussed. In the methodological part of the thesis, a controlled comparative approach is used. The software quality features from the ISO/IEC 25010 (Systems and software Quality Requirements and Evaluation, SQuaRE -Software product quality model) are used to compare the three groups of tools. The same procedures (import, aggregation and reconciliation of data, filtering, joining, detection of duplicate records, analysis of records according to Benford's Law) are applied on three different real datasets of accounting records anonymized. The empirical results in the practical part of the thesis evaluate the degree of functional suitability, performance efficiency and usability of the auditors' tools for processing of accounting data and propose recommendations for their combination and audit workflow.