Data analytics has been featured as a top-priority audit trend for the past five years, mainly because the need to achieve audit efficiency has been rising.
Business operations today are becoming increasingly complex. With this increase in business complexity, 控制程序和接触点, the amount of data a business handles is rapidly increasing. 这是审计的重要组成部分, 然后, becomes the ability to analyze this data and perform procedures to identify variances. This is w在这里 data analytics comes in—to allow an auditor to sift through large chunks of transactions in minutes and reveal trends that can inform auditors of the most significant risk areas. 不管我们接受与否, auditors now need to understand the basics of performing data analytics JUST to deliver what is asked from them.
然而, five years into this becoming a hot trend, most organizations are not even halfway through realizing the true benefits of audit analytics capabilities. 在一个 recent survey conducted on the internal audit functions across the UK and Ireland, it was indicated that a third of the organizations are yet to implement any data analytics solutions. But even the organizations that have made audit analytics investments are not seeing the kind of ROI necessary for such implementations.
Gartner在2018年做出了著名的预测, “到2022年, only 20% of analytic insights will deliver business outcomes.” And to this date, a lot of it is still true. Some of the top reasons why organizations still fail to implement a robust audit analytics practice are:
1. Not understanding the overall objective of implementing analytics or implementing just for the sake of it—A lot of organizations today include data analytics as a key pillar in their IA strategy, 然而, very few truly understand the objectives. What do we want data analytics to deliver at a departmental level, at a team level and at an individual audit level? Are we trying to reduce the time taken to perform an audit, or are we trying to gather deep insights by performing substantive analysis in the organization’s key risk areas? These are some important questions that need to be answered before any kind of analytics strategy can be decided.
2.Challenges with finding the right source of data and the right quality of data—Organizations today are dealing with huge amounts of data held within disparate systems and in dissimilar formats. IA teams hence often find it difficult to find the right source of data (or the source of truth) to begin with. Add to that the complexity of ensuring the accuracy, completeness and integrity of data in order to achieve the right results from their analytical procedures.
3. Skills/talent shortage and not seeking external support to build data capability within the team—T在这里 is a shortage of talent when it comes to data analytics capabilities that organizations often suffer internally. 然而, not being able to tap into external resources is detrimental to the overall penetration of analytics. External support could be in the form of consultants or even external training expenditure for their own teams. The key is to identify the gap and fill it to the best of the function’s capability—internally or externally.
事实是, every new advancement comes with its own set of challenges, and audit data analytics is one such advancement that is 在这里 to stay. It has the potential to make audits efficient, 少浪费时间, 更普遍的, 当然也更有影响力. None of these benefits can truly be realized unless we step back for a moment, 看看我们真正想要实现什么, 确定目前的差距, and 然后 find the appropriate support to bridge those gaps. That is when we will start seeing the real ROI from audit data analytics.
编者按: Find numerous IT audit tools and resources from ISACA 在这里.
