TIAPS ALB_Module 2E. Data Analytics for Internal Auditing

2E.2 Data Analytics Methods
Analytical methods can be grouped according to their main purpose.
• Descriptive methods are designed to report activity and often includes aggregating
and summarizing large amounts of data using averaging and other techniques for
making comparisons.
• Diagnostic methods are used to interpret information and identify likely causal
relationships and trends.
• Predictive methods are used to make forecasts by extrapolating known data and
creating models based on trends and known interdependencies and correlations.
• Prescriptive methods go one step further than predictive methods and suggest
actions to optimize future performance.
Prior to applying any analytical technique it will be important to validate the data and apply
data hygiene techniques, removing duplicates and inconsistencies. Unstructured data (like
emails, social media posts, contracts, and recordings of phone calls) must be organized and
structured before it is possible to process it. The analysis can only be as good as the data
you start with. Common types of data validation checks include:
• Data type check, confirming the entry of data in a data field is consistent.
• Code check, confirming data conforms to valid values according to set rules.
• Range check, confirming data falls within any set parameters.
• Format check, confirming data consistently matches defined formats.
• Consistency check, confirming logical consistency that matches the process or
activity recorded.
• Uniqueness check, confirming identifiers such as IDs or emails are unique. 64
The following analytical methods are described below and may be utilized manually or by
applying technological tools:
• Variance Analysis.
• Trend Analysis.
• Reasonableness Testing.
• Ratio Estimation.
• Benchmarking.
Many other methods (e.g., decision trees, time series, fuzzy logic) are also available.
2E.2.1 Variance Analysis
Variance analysis involves comparing two similar sets of data and attempting to find reasons
for any differences. Typically the comparison is between actual outcomes and one or more
of the following:
• Expected or desired outcomes.
• Predicted or forecast outcomes.
• Budgeted outcomes.
• Historical outcomes.
• Comparable benchmarks.
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See Data Validation, Corporate Finance Institute, 2023.
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