Risk
Ebook
Rising to the Audit Analytics Challenge: How to Prioritize Efficiency, Accuracy, and Value
Why analytics adoption is critical to enabling modern audit teams to manage heavier audit workloads and bigger control populations.
Three ways to make the case for investing in a low-code/no-code data analytics solution.
Two primary use cases for applying analytics automation in internal audit and SOX.
What's inside
While data analytics have a proven host of benefits when utilized effectively by audit teams, audit analytics adoption levels are still not entirely uniform across the industry. According to Chartered IIA and AuditBoard’s data analytics report, 60% of internal audit teams use data analytics. Meanwhile, the IIA’s North American Pulse Survey found 68% of CAEs would invest in more analytics if they had the resources.
At the same time, a CAE’s worst fear is making an investment in a technology solution only for it to get shelved months down the line due to low adoption rates and being too complex for day-to-day use. In AuditBoard’s guide, *Rising to the Audit Analytics Challenge: How to Prioritize Efficiency, Accuracy, and Value, we examine the two most common obstacles to analytics adoption: budget barriers and differentiating between high-code vs. low-code/no-code audit analytics.
Download this free guide to learn:
Why analytics adoption is critical to enabling modern audit teams to manage heavier audit workloads and bigger control populations.
Three ways to make the case for investing in a low-code/no-code data analytics solution.
Two primary use cases for applying analytics automation in internal audit and SOX.
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