Data analytics in internal audit: State of the data, 2019

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Back in 2008, I placed a talented senior IT auditor who was one of the first I had seen with excellent data analytics skills, an ACL certification, and a vision for how to apply data analytics to a broader suite of audits.

Our Fortune 500 client seemed very keen to capitalize on his skills. However, in the end, our client couldn’t clearly articulate a vision for Audit’s use of data analytics. The senior IT auditor moved to another company where Internal Audit had fully embraced this. Today, he is a senior IT audit & data analytics manager for a Fortune 100 company.

I’ve seen this story unfold more than once over the years. The takeaway: even with great people evangelizing the power that data analytics can bring, data analytics has taken a long time to take root within Internal Audit. With the seeds long planted, the garden is finally burgeoning forth.

Now is the time to embrace data analytics, people! But this isn’t the data analytics of even five years ago. The real power play today is coming from Python, R, and SQL. These are the tools IT audit professionals need to embrace and learn to use. Alteryx is on the horizon but doesn’t seem to have made big inroads into Internal Audit yet.

In the course of doing the research for this piece, I spoke with more than a dozen Internal Audit data analytics leaders and senior practitioners, at companies ranging from a major airline to a behemoth in the search engine and innovation space, to get their views on where things stand now, and what proactive IT auditors can do to hang ten on the data wave.

The current Internal Audit data analytics landscape

You might be wondering how embedded data analytics are at this point. Good question. My research shows that, in terms of the percentage of audits that use data analytics, the range goes from 25-30 percent to 50 percent, with at least two very large companies looking to be at 100 percent by this year.

How to get into the game

Build skills. How do you do that? The consensus among the leaders I spoke with is that online courses are a fantastic way to start.

Check out Coursera, Udacity and Datacamp. Take this first step and dip your toes into the water with their free course offerings on Python, SQL and more. Boot camps are another way to go if you have the time. Once you have some skills, take on a project at work – a small one that you can drive to an early win. Do you need some sort of analytics certification? The answer across the board was no. What you need is curiosity, fearlessness, some skills and a growing portfolio of projects to build a solid use case.

Other tools that are ancillary but useful to start getting your arms around: Power BI, Tableau, QlikView and Spotfire. What’s in the works: Robotic Process Automation, AI, Neural Nets.

Now’s the time to start reading up. Who makes the best data analysts for Internal Audit? IT audit professionals. Why? Because being an adept and successful IT auditor requires that one is able to translate complex technical topics for non-techies. Business acumen and business process knowledge come with the territory, as does customer-facing concern and interface.

What problem are you trying to solve? What data do you think will help you find an answer? IT auditors know the audit process and the evidentiary requirements for solid audit findings and recommendations. They also know how to write for a variety of audiences, which was identified by all the experts I spoke with as a critical skill.

Sure, data scientists know the tools inside and out, but they don’t have these other pieces, many of which are part of the intangible art of auditing.

(This post originally appeared on the ISACA blog, which can be viewed here).

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