On the Frontlines: Trends in Data Analytics
On The Frontlines Faizal Chaudhury, CPA, CIA, CGMA Sep 08, 2023
The biggest change I have seen in the use of data analytics in internal audit in the past decade is that it is no longer a “nice-to-have” but rather a “must-have.” This trend has been reflected in several surveys of CAEs, for whom the advancement of data analytics is a top priority.
This is particularly true for internal audit departments in heavily regulated industries such as financial services, where large amounts of data need to be analyzed but extra care must be taken because of the confidential nature of some of the data involved.
Internal auditors can use data analytics to help their organizations achieve tangible value. There are various ways value is achieved, including but not limited to:
- Enhanced data analysis: Organizations can analyze large volumes of data quickly and accurately, identifying patterns, trends, and anomalies that may be difficult for human auditors to detect.
- Improved risk assessment: By analyzing more data, auditors can better assess the likelihood of future risks and their potential impact on an organization.
- Enhanced communication and reporting: Data-driven insights can help internal auditors generate clear, concise, and accurate reports, tailored to the needs of stakeholders.
Data analytics helps auditors perform tasks that were previously implausible due to the amount of staff time required. For example, internal auditors have typically used samples in their testing because testing an entire population would be too time consuming. With data analytics, internal auditors can analyze the entire population of data quickly and efficiently, allowing for much better risk coverage.
Emerging technologies like generative artificial intelligence (AI) will also transform internal audit and its use of data analytics. Generative AI can help democratize data analytics to a certain extent. While data analytics allows organizations to analyze large volumes of data quickly and accurately, this activity requires specialized software and analysts with advanced skills, such as programming. Generative AI will level the playing field by enabling organizations with fewer resources and less specialized software and skills to incorporate data analytics.
As with most emerging technologies, it is important to have a good understanding of AI before creating a roadmap to unlock value for your organization. There is no one-size-fits-all approach, so identify use cases first and establish a pilot program to validate assumptions or hypotheses before scaling up use more broadly across the organization.
Faizal Chaudhury will be presenting a session on data analytics at The IIA's 2023 Financial Services Exchange on September 12.