How firms are delivering value with audit data analytics

By Ken Tysiac

When Ed Wilkins, CPA, talks to audit committees about adding data analytics to the audit process, he explains to them that it usually takes three years for investment in a complex audit analytic to pay for itself.

Audit analytics give practitioners the ability to examine an entire dataset rather than just a sample, improving the quality of an audit.

“Doing 100% populations, that’s a big thing,” said Wilkins, a partner in Audit & Assurance Services with Deloitte & Touche LLP who co-leads Deloitte Audit’s analytics offering. “… As a partner I get a lot more comfort looking at 100% of the population.”

Ultimately using the analytics enables the auditor to survey an entire population more efficiently than he or she could examine a sample during a manual process. But the cost/benefit decision is something audit committees need to be educated on, Wilkins said. While his group is involved mostly in the design/deployment of analytics and data wrangling, the technology costs are a separate element to discuss as those costs are quite high.

He tells audit committees that in the first year of implementing a tailored analytic designed specifically for their company, it takes extra time for the company to locate the dataset and more time for the auditor to get the data reconciled and formatted (typically referred to as data wrangling) and prepared to go through the analytics process.

This adds hours to the audit in the first year.

“The second year we hope to break even in hours,” Wilkins said, “because now we’ve got the data-wrangling piece identified, the client has it down and documented how to get us that data, so it becomes more efficient on their part, we know the mapping better. But there are still going to be a few tweaks.”

By the third year, the process typically is established to the extent that the full-population analysis consumes less time than sampling. And the added benefit comes with the identification of outliers or notable items.

For example, consider an analytic that audits a company’s stock compensation for its employees. Through sampling, an auditor could identify that the company generally is appropriately following its procedures and formula for employees.

But a custom or tailored analytic can enable an auditor to discover if there are any employees among the hundreds or even thousands at a company whose stock compensation has been inadvertently been set up incorrectly.

The analytic process often requires less time for the client as well as the auditor once it is established, Wilkins said.

“When we do sampling we’ll give the client the list [of items or transactions to be sampled], and they have to pull all the transactional data for us,” he said. “Now they give us a file. … We’re actually finding in many cases, we’re saving the client more time than we’re saving ourselves.”

Clients who see the benefits of applying audit analytics to some transactions often choose to incorporate additional areas into the process, Wilkins said. For CPAs in the audit profession, audit analytics present an opportunity to improve their services, but they also present a challenge.

Applying analytics to audits requires a new skill set that practitioners must develop. When Wilkins speaks with college and university professors, he explains that it’s not necessary for auditors to be programmers because writing the equivalent programming scripts for data analytics will be handled by data specialists rather than CPAs.

Instead, auditors will need to be conversant on the subject of programming so they can describe what analytics of a given transaction need to accomplish. Auditors also will need critical thinking skills so they can analyze how analytics of a given dataset can improve the audit and potentially lead to decision useful information for the client.

“That’s what we’re looking for,” Wilkins said. “Get enough of that experience so they can sit back and critically think about that dataset. What stories should be coming out of the data? How do I get that information, and now be conversant with the data specialist who is now part of our core audit team to be able to tell that story?”

The enthusiasm for this subject on the part of those in accounting academia has Wilkins confident that college students’ training — in addition to their understanding of technology — will prepare them well for this kind of work.

A bigger challenge, perhaps, is preparing more-experienced professionals to apply analytics when they have been performing sampling for the duration of their careers. To provide the highest quality service possible, auditors will need to learn to apply analytics techniques.

Firms may accomplish this by committing to training for their existing audit workforce. For individual auditors, becoming proficient in data analytics may require seeking out classes and online training.

“They’re going to have to read,” Wilkins said. “They’re going to have to teach themselves. Most likely they’re going to be hiring a few people as well that they can learn from. But again, they really need to be conversant in it to effectively work with specialists. At Deloitte, we have added a significant number of CPE hours in this area to supplement all of our mandatory training already required for data and analytics.”

Ken Tysiac ( is the JofA’s editorial director.

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