Source:- AccountingToday, Author Anthony Cecil
Today’s college students were born into the Information Age, and simply cannot imagine a world without technology. During my decade-long experience teaching graduate courses, students are increasingly cognizant of using technology to help them find answers quickly and replace manual tasks.
The younger generation of tech-savvy professionals is eager to apply technology to glean more from data and deliver deeper insights, if given the opportunity. As an educator, we cater to our students’ intrinsic use of technology by incorporating data analytics into the curriculum.
The Utica College masters program requires a minimum of five years of professional experience, so many of our students are employed full time while working toward their master’s degree. Graduate students who are actively working as auditors or investigators quickly grasp the concept of using data analytics in their work. They immediately latch on to its ability to gather and analyze large amounts of information, and see its value in helping them make more informed business decisions.
Unfortunately, their enthusiasm turns to discouragement when they approach management about using purpose-built data analytics tools. The objections are common across all industries. Educating management often falls on young professionals who make some valid points, if given the opportunity to be heard.
Excel often becomes the default analytics tool due to convenience and familiarity, but is not advantageous when conducting a thorough, professional investigation
Capacity: Excel’s record, row, column and worksheet limitations prohibit users from analyzing the complete data population, at least in one pass.
Functionality: When analyzing data to identify patterns, correlations and potential fraud, purpose-built data analytics tools offer functionality capabilities that speed up complex analysis.
Acquisition: Bringing data in from multiple data sources and systems into Excel is challenging and tedious. It requires significant data clean-up to prepare it for analysis.
Integrity: Data values can easily be altered by mistake or deliberately. Formula errors, duplication and security are also top concerns when providing assurance to the client or stakeholders.
Complexity: Testing and automating repeatable tasks require advanced programming skills.
While Excel and Access are loaded on just about every computer, they are not adequate for most complex data analytics work. Sophisticated data analysis technology should be a staple in every auditor’s and investigator’s toolbox. It allows you to bring in virtually limitless amounts of data from different sources into a single tool for analysis. Once imported, data cannot be altered and audit trails record every activity, which provides assurance in the findings being presented.
While data analytics tools have been around for about 25 years, they have evolved significantly with each new release. In fact, many now offer point-and-click capabilities and all offer free how-to videos, technical support and help resources embedded within the software.
The academic community is actively working to give accounting, auditing, finance and forensic students experience using data analytics tools prior to entering the workforce. It typically takes two weeks or less to grasp the basics of using a purpose-built data analytics tool. Graduate students who have been analyzing data manually are surprised at how much faster it is to complete the same task using an analytics tool.
One Utica College graduate student, Larry Bateman, was first introduced to data analytics technology more than 27 years into his career. Bateman has worked as a special agent and financial investigator for the U.S. Department of the Treasury, and currently works as a financial crimes consultant.
“I have used Excel and other Microsoft Office applications extensively in my work, but after seeing [data analysis tool] CaseWare IDEA, I realized how valuable it would have been for financial investigations,” said Bateman.
Most data analytics tools are simple and intuitive. They simplify using equations, custom functions and scripts to dig through large data sets in search of anomalies, and easily automate repeatable tasks. The time savings is well proven. Furthermore, not everyone has to learn data analytics techniques. Students and younger professionals, who tend to pick up the techniques early, and are in an ideal position to share their knowledge with others in the organization. Some organizations take this a step further by designating “super users” who attend hands-on training and other educational opportunities to help guide others in their use of data analytics.
“Our auditors can now do in minutes what it took hours and hours and hours in Excel,” said a senior director of internal audit. “They can get more value-added work done with fewer resources. Data analytics tools can quickly process five to six years of data, a tremendous amount, and summarize it into quarters by vendor.”
My advice to clients learning data analytics is to use data from an audit or investigation they are working on to help learn the ins and outs of the data analysis software. This work-study process promotes learning to use the tool while working on data for an engagement so no time is lost.
It’s about working smarter by spending less time on manual tasks, which enables staff to focus on more value-add work. Technology helps professionals make intelligent judgments about which documents to inspect, and gives them greater coverage of all available data by processing 100 percent of the data population. Without it, management is missing an opportunity to be more efficient.