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Why Excel Is Not Enough for Effective Data Analysis

Today’s business world increasingly runs on “Big Data” — exceptionally large sets of digital information. Companies analyze this data to discover patterns and trends, leading to new-found efficiencies and, ultimately, better bottom lines. 

 

For modern internal audit, the benefits of leveraging data analysis are essential to increasing audit quality and efficiency. Previously time-intensive operations can now be performed routinely and with lightning speed thanks to dedicated auditing software platforms

 

Data analysis allows today’s internal auditor to better understand business trends and patterns by moving beyond just text or numerals to employ visual representations, like graphics and dashboards. Freed from analyzing routine data, auditors can focus on performing better testing, addressing anomalies, and examining areas of risk.

 

No matter what size a business may be, data-driven audits mean better, more efficient audits. 

 

Bigger Data, Better Accounting Data Analysis

 

Data analysis is the systematic examination and evaluation of data, and the drawing of insights from it. The concept has long been part of the traditional internal audit process. An auditor tracking duplicate payments for purchase orders across journal entries, for instance, uses analytic techniques.

 

For many years, spreadsheet programs like Microsoft Excel have been the go-to tool for auditors. However, times are changing — for the better. Today, specialized tools now aid auditors in working with huge information repositories that manual auditing techniques are not able to handle. Firms risk being left behind by competitors if they are slow to take advantage of this exponential improvement in auditing capability risk.

 

The digitally driven needs of the modern auditor reveal the limitations of conventional software tools such as Excel. As data sets grow larger, today’s question is not whether an organization will introduce data analytics to internal audit but how best to do so. 

 

Your audit process can benefit in key ways by moving away from the limitations of Excel and letting automation serve as a gateway to more efficient auditing and monitoring processes.

 

Benford’s Law Analysis

 

Benford’s Law, also known as the Law of First Digits, is a statistical law with big implications for data analysis. It holds that the leading digit is likely to be small in many naturally occurring data sets. So, the number 1 will appear as the lead digit about 30 percent of the time, and other digits will appear with decreasing frequency, with 9 appearing least frequently as the leading digit (less than 5% percent of the time).

 

Similar to the application of normal distribution, Benford’s Law can be used as a tool for detecting patterns (or lack thereof) in naturally occurring data sets. This can be a powerful means of catching anomalies or detecting fraudulent activity. However, applying Benford’s Law to a large data set can be challenging in software like Excel. It  is simply not designed to map this kind of distribution.

 

Traditionally, data analysis has relied on sampling from large data sets held in spreadsheets. Auditors draw conclusions from these samples and the their knowledge of the company.

 

However, using an incomplete data set introduces the potential for error, as crucial data can be missed. Likewise, relying on an external auditor’s own knowledge of the company may lead to mistakes if that knowledge is limited.

 

Data analytics software, on the other hand, can test entire data sets, no matter their size. This avoids potential errors that can result from limited sample sizes and allows teams to perform more thorough audits. Data analytics can also flag anything unusual for further review, rather than relying solely on an auditor’s potentially incomplete knowledge of a client’s business.

 

Automate and Accelerate Testing Procedures

 

Using traditional methods, auditors have had to perform data analysis separately or rely on additional data specialists. They experienced longer, more expensive audits and gained little visibility into the tests they performed.

 

Bringing data analytics into the audit workflow to automate testing is a significant benefit. Using artificial intelligence and machine learning, analytics software can quickly and accurately examine all transaction and balance entries. This provide meaningful results for further review. 

 

Automate Mundane Tasks and Focus on Key Risks

 

One of the biggest improvements data analytics tools bring over the traditional spreadsheet approach is the automation of mundane tasks. With software, auditors can employ ‘scripts’ to instruct the program to examine whether internal controls have been breached. As well as reducing the impact of human error — inevitable in a manual review — the script makes the action readily repeatable. 

 

Likewise, teams can eliminate the risk of data corruption using automation. This pitfall can only occur accidentally during the manual transfer or extraction of data for internal audit processes. This automation frees the auditor from rote tasks, leaving them focused on value-added audit activities.

 

Moving Beyond Excel

 

Automation is the gateway to continuous auditing and monitoring and is rapidly becoming an indispensable part of audit workflows. To find out more about non-Excel-based solutions and how CaseWare IDEA can help enhance your audit process, download our free whitepaper, Beyond Excel, today.