Using internal audit analytics can be a great way for an audit team to better identify risks and strengthen their overall audit methodology. And while internal audit analytics may seem like something reserved for only the most technical audit leaders, analytics software increasingly makes it easier for all types of auditors and assurance professionals to conduct deeper data analysis.
- What is audit analytics?
- What is the difference between data analytics and audit analytics?
- Who uses audit analytics?
- When can audit analytics be used?
- What are the advantages of audit analytics?
- How to implement audit analytics
What is audit analytics?
Audit analytics, or audit data analytics, means the intelligence generated from reviewing audit-related information, often through the use of technology. Like other types of data analytics, audit analytics typically involve analyzing large sets of numbers (but could involve text) to find actionable audit insights.
What is the difference between data analytics and audit analytics?
Audit analytics is a subset of data analytics. All audit analytics are data analytics, but not all data analytics are audit analytics. Put another way, audit analytics are audit-related data analytics, but data analytics can also apply to other business functions.
As Experis Finance explains in a report published by The Institute of Internal Auditors (IIA), the broader category of data analytics means: “The process of inspecting, cleansing, transforming and modeling data with the objective of highlighting meaningful information, suggesting conclusions, and supporting decision making.”
