Data about your organization can be a powerful tool, and internal auditors are increasingly taking advantage of a variety of technologies like IDEA Data Analysis to incorporate data analytics in their audits. Yet, the use of data analytics can easily go awry if the insights that they bring to the decision-making process are not properly interpreted.

It is this fear of misinterpreting data with potentially ruinous consequences that have many chief audit executives (CAEs) unsure about using data analytics as a part of their standard audit process. According to The IIA’s 2018 North American Pulse of Internal Audit, only 62% of CAEs reported that they have partially or fully implemented data analytics in the audit process. (An improvement on 42% in 2017.)

By ensuring that proper training and processes are in place to limit the potential risks of data misinterpretation through data analytics organizations are able to ensure they maximise their return on investments and provide their clients, both internal and external, with 100% audit coverage and assurancy. Here are six key ways auditors can implement data analytics in internal audit:

1. Clean the data

Data used for analysis must be correct, consistent, complete, and free from duplication with inaccurate or irrelevant parts deleted. The resulting “clean data” is easier to combine with different datasets to gain deeper insights and more thorough analysis.

2. Deal with outliers

Just because 99.5% of transactions in your last audit gave a positive return doesn’t mean everything is going well. That 0.5% of transactions might well equate to over half of a company’s total losses in a given period. Outliers should not be ignored, they should be understood. They may be telling you something important. Seize the opportunity to discover the reason why things didn’t come out the way you thought they would.

3. Accurately read patterns and eliminate “noise”

Data that is not stable or has a high level of variability will not allow you to compare, predict, and forecast correctly. Eliminating noise or corrupt data will also allow your data to be correctly used by machines. With some patterns too complex for most humans to detect, data analytics solutions are rapidly being developed or improved upon with built-in intelligence to enhance the capacity to detect meaningful patterns.

4. Clearly visualise the data

Detailed and revelatory audit reports are no use to anybody if the data isn’t be read and actioned upon. With key decision makers unlikely to read pages of text and numbers that blur into one, it is vital that auditors communicate results succinctly and clearly. Visualisation; graphs and charts carefully constructed to convey the key information should be leveraged early in the analysis process in order to identify areas of interest quickly and save time in the overall process.

5. Understand correlation versus causation

Correlation describes the relationship between two variables, while causation speaks to the idea that one event is the result of the occurrence of the other event. It is easy, and too common, to assume causation when there is simply correlation in the data and individuals viewing the data will be influenced by past experience and their own personal biases.

6. Recognize when you should not use data

More data isn’t always better, as not all data will help achieve audit objectives. As stated in our previous blog, identifying what data is necessary and restricting yourself to only that data limits the potential for biased results. Also, similar to correlation versus causation, data can often appear to have more meaning than it really does. Beware the danger of jumping to conclusions that ultimately may not be supported by the data.

When it comes to truly understanding and leveraging your data, a number of skills are required; there is growing recognition that numerous related but distinct roles are necessary, including data scientist, data analyst, data architect, and more. Internal audit will soon need to consider adding these types of roles to the department (some larger departments have already done so). Until then, it is critical that auditors at least add foundational data analytics skills to their toolset in order to capitalise on the data analytics investments of their company.

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