Audit data analytics

Audit data analytics involves the analysis of complete sets of data to identify anomalies and trends for further investigation, as well as to provide audit evidence. This process usually involves an analysis of entire populations of data, rather than the much more common audit approach of only examining a small sample of the data. With the more thorough analysis offered by data analytics, an auditor can benefit in the following ways:

  • Better advance planning, since analytics can be used early in an audit to identify problem areas.

  • Better risk assessments, based on any anomalies and trends uncovered.

  • Higher-quality audit evidence, since the auditor can now examine far more data than had previously been possible with audit sampling.

  • The communication of more issues to the client, since data analytics is more likely to uncover a variety of anomalies that could be of interest to those charged with governance of the client.

Despite these benefits, the use of audit data analytics can be restricted by the inaccessibility or poor quality of client data, or of data that cannot be converted into the format used by the auditor’s data analytics software. Also, the use of data analytics requires a new set of competencies in which auditors may not have training or experience. And finally, smaller audit firms may not be able to afford the cost of audit data analytics tools.

Nonetheless, audit data analytics represent a significant improvement over traditional audit techniques, and so will likely occupy an increasing proportion of auditor time in the future.

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