A Road Map to Data Analytics for Internal Audit

IBM estimates that approximately 90 percent of all the data in the world was created in the past two years alone.

Organizations now capture and process greater volumes of data than ever before.

Only a few years ago, working with a 100-megabyte file was considered a lot of data. Today, data can be measured in zettabytes, or ZBs, which is equal to 1 trillion megabytes. In 2012, 2.8 ZBs were created; in 2020, the total data generated annually is forecasted to reach 40 ZBs.

Organizations are “drowning in data, but starving in knowledge” The key sticking point is setting up the system around how to harness that data. These more advanced data analytics techniques could help internal audit achieve more meaningful results. Data analytics is “the process of gathering and analyzing data and then using the results to make better decisions”.

According to the research, internal audit professionals use data analytics for four primary tasks, compliance, fraud detection and investigation, operational performance and internal controls assessment.

The research revealed that an organization’s maturity for data analytics is determined by its vision for data analytics in three categories:

• People (the ability of internal audit team members to embrace data analytics);
• Process (the method an organization uses to access data and integrate it into its daily tasks)
• Technology (the platforms, systems, and tools that enable data analytics)

Internal audit functions that have integrated data analytics into their operations point to a number of tangible benefits. These benefits fall into four broad categories greater efficiency, better informed decision, risk mitigation, monitoring, measurable cost savings.

The technology currently used by internal audit departments varies widely in complexity and functionality. In the Data Analytics and Internal Audit Survey:

• 77 percent of respondents reported using Microsoft Excel for data analysis
• 53 percent deploy computer-assisted audit techniques (CAATs) such as Audit Command Language (ACL) and IDEA
• 37 percent use Microsoft Access
• More than 10 percent indicated other tools, including SQL, Tableau, and SAS
• Only a handful of respondents have adopted other tools such as TeamMate Analytics, Monarch, ActiveData, Oversight, SPSS, EnCase, Arbutus, ActiveAudit, EZ-R Stats, and Forensic Toolkit (“FTK”)

Based on some of the CAEs experience during this product’s development, several important considerations emerged:

• Can the tool be linked to a production system so that the data can be obtained on demand?
• Is the tool cross-functional (i.e., able to cleanse, validate, test, and visualize data)?
• What is the total cost of ownership (including purchase price, maintenance, licensing, and employee training, among other factors)?
• How quickly can employees get up to speed?
• Can the tool access data which is structured and unstructured?
• Can the tool be hosted on the cloud? Considering vendors and tools with both on premise and cloud solutions provides more options.
• What tools are already being used by other departments? Before purchasing a new tool, CAEs should communicate with other executives and managers to evaluate whether other departments use tools that could be expanded to internal audit.

Below list is from most to lest challenges:

• Difficulty in obtaining, accessing, and/or compiling the data
• Time required to develop and execute analytic procedures
• Insufficient resources or the need to train personnel
• Lack of understanding about data analytics
• Lack of management buy in
• Inability to interpret results

Internal audit leaders must develop a vision as a first step for how data analytics can serve their organization and then ensure they have the people, process, and technology to execute effectively.

Internal audit departments currently use data analytics for four primary tasks: compliance, fraud detection and investigation, operational performance, and internal controls.

Internal audit functions that have integrated data analytics into their operations point to a number of tangible benefits. These benefits fall into four broad categories greater efficiency, better informed decision, risk mitigation and monitoring, measurable cost savings.

Source: The 2015 Data Analytics and Internal Audit Survey (The IIARF and Grant Thornton).

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ABOUT THE AUTHOR

Arif Zaman brings with more than a decade of proven experience in internal audit, risk management and fraud investigation. He is the Head of Internal Audit at Private Joint Stock Company based in Dubai, UAE. He holds a MSc in Professional Accountancy from University of London and BSc Hons in Applied Accounting from Oxford Brookes University along with an impressive set of professional certification including ACCA, CIA, CISA, CFE, CCSA, CRMA, CRBA, CPA and CGA etc.

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