3 challenges of implementing internal audit data analytics

Ryan C. Luttenton, Ryan Singer
10/8/2021
3 challenges of implementing internal audit data analytics

Not every data analytics project is destined for success.

Data analytics is the next big thing for internal audit (IA), but internal audit data analytics projects often fail to yield a significant return on investment because many organizations run into one or more of the following fundamental challenges during implementation.

1. Without a clear vision, data analytics projects can flounder.

Without a clear vision, data analytics projects can flounder.

Data analytics can introduce a lot of new variables and unknowns in the internal audit process, which might make it difficult to know where to start. Many organizations begin with good intentions but lack a logistical project plan that details exactly how they will implement data analytics in a way that solves real problems and advances their business goals.

A practical data analytics project plan should answer questions such as:

  • Who owns this project, and who will keep it on track?
  • What is our ultimate goal for internal audit data analytics, and how will we measure our progress toward that goal?
  • Who are the stakeholders, and whose buy-in do we need?
  • What skill sets are required for this project?
  • Which analytics do we want to see and why?

Internal conversations can help clarify answers to these questions. Additionally, talking with people in other internal audit departments and at peer organizations where a data analytics plan is in place and inquiring about what they’re doing, how they’re doing it, what’s been successful, and what lessons they’ve learned can provide insights.

2. If internal audit data analytics aren’t chosen thoughtfully, the results won’t produce value.

If internal audit data analytics aren’t chosen thoughtfully, the results won’t produce value.

Before starting a large-scale data analytics project, internal audit departments need to ask: What do we want data analytics to achieve for us at a high level? If a meaningful answer to this question doesn’t emerge, or if the data analytics don’t serve the answer that emerges, it’s easy to end up with an analytics output that looks nifty but delivers little value.

When the internal audit team at Crowe created our solution for data analytics, we asked that high-level question. The answer that ultimately emerged was that data analytics should make internal audits more meaningful and efficient.

From there, we targeted specific elements of the audit process for improvements that would support the larger goal. We decided to move from sampling essentially at random to selecting specific auditable areas, work program steps, and samples that carry a higher risk of error or impact.

Regardless of how an organization approaches the specifics of its data analytics implementation, stakeholders and clients should be involved through an iterative process. Consulting with a diverse group and staying adaptable throughout the data analytics journey can help produce practical solutions that create real value.

3. Organizations might not want to devote the time, money, or expertise necessary to implement internal audit data analytics.

Organizations might not want to devote the time, money, or expertise necessary to implement internal audit data analytics.

Internal shops tend to run lean, so there’s not a lot of excess capacity available for the next big data analytics project. Most likely, additional resources will be necessary for any large-scale implementation of internal audit data analytics.

Before assessing gaps in resources and expertise, organizations need to decide which path to take. One important question to ask: Should we buy a data analytics solution or develop one in-house?

Purchasing and implementing a solution still requires time, money, and expertise, but the lift is a lot lighter than developing software in-house. Larger organizations with abundant resources or shared services might decide to build out a solution from scratch. Most smaller organizations probably will choose to purchase.

After choosing the best path, organizations then need to develop a resource plan and dedicate a team. Once the required resource needs have been assessed, the decision boils down to cost versus benefit. Is it worth committing these resources to have data analytics in play?

If your organization decides to move forward with internal audit data analytics, it might be possible to cut costs through efficiency. Still, your organization ultimately will need to back up the data analytics vision with the resources required to get the job done.

Visit our webpage for Crowe Analytics Advisor to find out more and schedule your 30-minute demo.

What internal audit data analytics challenges are you seeing?

The internal audit data analytics team at Crowe understands these challenges because we tackled them as we created our solution, Crowe Analytics Advisor for banking.

Whatever you’re looking to accomplish, whether you need to outsource development of a data analytics solution or consult with specialists to help formalize and advance a data analytics project, we’ve got smart, experienced people who would love to step in and assist.

Let’s get in touch and talk about your current internal audit data analytics challenges and goals. You can drop us a line anytime.

Contact us


Ryan-Luttenton-225
Ryan C. Luttenton
Partner, Consulting
Ryan Singer
Ryan Singer
Consultant