Today’s organizations increasingly process and rely on data to make critical operational and strategic decisions. As the business’s use of data increases, so too do the expectations for audit to incorporate data throughout its work. In response, audit leaders have made significant investments to help their teams perform more data analytics in all phases of audit work — but with limited success.
"These investments have helped audit increase the quantity of data analytics performed over the past few years,” says Tegan Gebert, Senior Director, Advisory, Gartner. “However, quantity doesn’t equal quality: Eighty percent of audit departments report that they haven’t achieved the expected level of insight from their data-driven work.”
Most audit leaders see this challenge as a skills mismatch between the data demands of audit work and the current skill sets on their teams. As a result, they look to talent solutions — specifically hiring and training — to drive more data-driven insights. But there are challenges to hiring and training for data-skilled auditors.
In need of capabilities needed to deliver insights
To remain relevant and add value in a data-rich environment, internal audit teams need to acquire new capabilities to deliver data-driven insights — findings that are material, newsworthy and actionable, not just observations based on data.
Many audit leaders try to build a data-driven organization by hiring and training for data skills, but this approach is time-consuming and costly. The demand for highly skilled, data-driven talent is high, making hiring difficult, and when good candidates are secured, they have a higher rate of attrition due to strong competition both internally and externally.
Better allocation of talent accounts for 62% of the potential improvement in audit’s goal of delivering data-driven insights
Training audit talent to deliver data-driven insights is no easier than hiring. Many organizations have ramped up training on specific data analytics tools, techniques and concepts, but the quality of audit’s work has barely improved. The training is ineffective because it is often overwhelming in the number of concepts covered, detached from workflows and positioned as another task on auditors’ growing to-do lists.
Audit leaders need their staff to exhibit data literacy, but without clearly articulating what data literacy means, they make it difficult for auditors to learn and execute. At the same time, new skill needs are shifting too fast for auditors to keep pace. The 2018 Gartner Shifting Skills Study finds only 44% of the skills, concepts and processes learned during training are applied on the job.
Learn more: Data-Driven Insights for Audit
Do more with what you already have
Instead of focusing on how to hire or train auditors in data skills, leading internal audit departments have found a way to use their existing data resources more effectively.
By making audit planning data-first, unlocking latent components of data literacy in the audit team and using data specialists’ time better, audit leaders can make the most of the talent they have today and deliver actionable insights.
Better allocation of talent accounts for 62% of the potential improvement in audit’s goal of delivering data-driven insights — insights that senior leadership are more likely to use.
Best yet, allocation is under the control of audit leaders and can be quickly implemented to deliver results, while longer-term hiring and training solutions have time to yield results.
3 focus areas for audit leaders
To reinvent talent operating models and reallocate data talent, focus on three actions — all within the control of audit:
- Make departmental audit planning data-first. Assess what datasets exist that could be used to drive insights — and pull forward that evaluation to the audit planning stage, rather than waiting until the plan has been set.
- Unlock latent components of data literacy in audit engagement staffing so that every audit engagement can be staffed with a team that collectively is data-literate.
- After better using the audit team, rebalance the non-engagement time spent by data specialists. Dedicate data specialist time to more advanced data projects beyond audit engagements.