Internal Audit Data Analytics

Technology continues to advance at the speed of light. Keeping tabs on the zettabytes of information in a digital data-driven society is akin to exploring new worlds and seeking out new civilizations.

As Spock would suggest, the “logical” response is to develop a system for using the data to increase or enhance business objectives. The response: Data analytics.

What is Data Analytics?

In general, data analytics is a process of examining and analyzing big data, then transforming those data sets into usable information that offers valuable insights. Because internal audit departments rely heavily on mining data in the overall audit process, internal audit data analytics is becoming increasingly necessary.

Within the context of audit planning, data analytics has been given a more succinct and detailed definition.

The American Institute of Certified Public Accountants (AICPA) defines audit data analytics this way: “The science and art of discovering and analyzing patterns, identifying anomalies and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling and visualization for the purpose of planning for performing the audit.”

Let’s explore this strange new world in more detail.

What is Data Analytics for Internal Audit?

Just as a Tribble infestation caused some major problems aboard the Enterprise, the exponential growth of available data constantly increases the complexity of the internal audit process. By incorporating data analysis into the internal audit function, Chief Audit Executives and internal audit teams can wade through the overwhelming amounts of data. They’re able to more effectively extract only the information they need to produce more robust and in-depth reporting.

Data analytics creates more value from the overall internal audit process. Evaluating the effectiveness of an organization’s internal controls, conducting risk assessments, and monitoring compliance materials realize huge improvements in quality and efficiency when data analytics are applied to the audit process.

Why use Internal Audit Data Analytics?

Based on the AICPA definition of audit data analytics, there are three ways data analytics increases the efficiency of internal audits.

Analyze patterns

Recognizing patterns within business processes is valuable in detecting efficiency as well as areas of weakness.

Using data analytics tools, internal auditors can test an entire population of data within a business process rather than examining a sampling. Not only does this help auditors identify areas of high risk within the organization more effectively, but also serves as a predictive measure, aiding internal auditors in recommending preventive internal controls.

Analyzing data patterns also offers more detailed analyses, assisting management in decision-making strategies, increasing the efficiency of overall business operations.

Identify anomalies

Risk management is one of the overriding purposes of the internal audit process. Using data analytics, internal auditors can initiate continuous auditing and monitoring and gather real-time data. Issues can be flagged, and more immediate action can be taken to mitigate risks.

The potential to completely avoid the wrath of Khan can be a game changer.

Extract other useful information

Automating the audit approach with a data analytics program increases the amount of information the audit department can access.

The ability to increase audit scoping opens new avenues for gaining valuable business insights. By expanding their universe to include more than the traditional scope of internal auditing (operations, financial services, compliance), audit departments can evaluate business operations on a larger scale. Often these new insights result in direct financial benefits.

Who Performs Internal Audit Data Analysis?

With the advent of advanced analytics programs, the need for analytics professionals has increasingly become a focus for many enterprises. Studies have shown that early adopters of internal audit data analytics have gained a competitive edge.

The ability to understand how to apply data science to internal audit methodology is a common reason many organizations have not fully implemented data analytics functions into their internal audit planning.

Because data quality plays a fundamental role in the success of an internal audit data analytics program, a specialized skill set for understanding and managing data analytics capabilities is necessary.

Captain James Tiberius Kirk wouldn’t have been able to get out of many a predicament without the occasional assistance from an ensign. The ideal structure for a data analytics team includes a combination of seasoned internal auditors and data analytics specialists.

If your organization is prepared to boldly go where a vast majority of organizations haven’t gone before (or yet), you’re in the position to take giant leaps ahead of your competition. At ZenGRC we’ve helped many organizations streamline their processes. Find out what it takes to manage risk and compliance with confidence.