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Leveraging the Four Types of Analytics in Internal Audits
Rachel Nelson, Associate Director, Data Analytics and Automation, Internal Audit, Chewy
The four types of analytics and how they translate to internal audit
There are four types of common data analytics: descriptive, diagnostic, predictive, and prescriptive. These data analytics also represent a maturity model with descriptive being the easiest to start with and then maturing to the other types of analytics.
Descriptive analytics is the easiest to implement in internal audits. Descriptive analytics tell you what happened in the past. Descriptive analytics are great for quantifying the value of an engagement letter or determining the scope of an audit. Performing descriptive analytics may include combining historical data from multiple data sources to get a full picture of what happened.
Diagnostic analytics provides insight into why something happened by slicing and deciding your data into different views and scenarios. This is where data analysts drill down into the data to find dependencies and identify patterns.
What it may look like in practice- In fieldwork, a correlation analysis is performed, and results are delivered to the audit team showing what key factors such as department, store, or salesperson are correlated to the transaction amount. Any strong correlations are then called out in the final audit report to strengthen the internal audit's findings and conclusion by proving them through the data.
Predictive analytics predicts the future based on the past. Regression is the most commonly talked about prediction model for internal audit, but there are also great benefits in decision tree and clustering models. To predict, you need to have solid historical data and previously identified cases and data of what you are looking to predict.
Regression is the most commonly talked about prediction model for internal audit, but there are also great benefits in decision tree and clustering models.
The purpose of prescriptive analytics is to determine what to do to mitigate risk. Gathering data for prescriptive analysis can be difficult as you may need a combination of both internal and external descriptive, diagnostic, and predictive data.
What it may look like in practice- Through the understanding of correlated variables, predictive models, and a-b testing, internal audit has determined what levers the business can push and pull to mitigate the risk of the transactions. The internal audit uses this data to provide a recommendation to the business.
Practice being agile by figuring out what works and doesn't work for your internal audit department and continuously adjust your approach. I hope you have found this article insightful and are excited to start on your own analytical journey.