Audit data analytics definition
/What is 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.
Who Uses Audit Analytics?
Audit analytics can be used by both internal auditors and external auditors. This works especially well for internal auditors, who can use continuous auditing tools linked to company databases to automatically examine every transaction entered into the system. The result is the immediate flagging of issues, which can be brought to the attention of management for immediate remediation. External auditors will probably not have such comprehensive access to all company transactions, but could use the internal auditors’ systems to conduct analyses.
Advantages of Audit Data Analytics
With the more thorough analysis offered by data analytics, an auditor can benefit in several ways. For example, the auditor has better advance planning, since analytics can be used early in an audit to identify problem areas. It also results in better risk assessments, based on any anomalies and trends uncovered. In addition, it yields higher-quality audit evidence, since the auditor can now examine far more data than had previously been possible with audit sampling. Finally, it results in 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.
Related AccountingTools Courses
Guide to Data Analytics for Audits
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Disadvantages of Audit Data Analytics
Despite the preceding 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.