8 tips for teaching Big Data 

Learn what you need to know about presenting this increasingly important subject to your students. 
by Cheryl Meyer 
Published January 12, 2016

Gone are the days when accountants only analyzed historical information. Today’s CPAs and accounting students need to tackle something much larger: Big Data.

“With the explosion of data that our client companies are creating, the profession sees the need for future CPAs to have a strong background in the techniques and tools required to sift through and evaluate that data effectively,” said Mitchell Wenger, Ph.D., an assistant professor of accounting at the University of Mississippi. 

The field of Big Data and data analytics requires skills in business analysis, data science, and computer algorithms, he added. The Association to Advance Collegiate Schools of Business (AACSB), an accreditation body, has also “mandated increased emphasis on analytic skills as part of the accounting curriculum,” he said.

At a minimum, Wenger said, today’s students need a basic exposure to Big Data and data analytics through accounting information systems programs, with the option to add more Big Data electives to their degree program.

Being conversant with Big Data, as well as gaining the advanced analytical skills needed to work with it, can give graduates an edge in the job market.

“In our practice we look specifically for people who have that Big Data experience,” said Tim Bryan, CPA/CITP/CFF, a director in the Forensic Accounting & Technology Services division at Crowe Horwath LLP. Students who understand Big Data, he said, “can be employed within our risk practice, performance practice, and forensic practice. Even my younger staff members are taking courses right now on data analytics. We need it.”

Teaching Big Data, however, can be challenging, as many accounting professors have not had firsthand experience with the topic, and sample curriculums and textbooks in the subject are not readily available. Here, accounting faculty share their best advice for becoming conversant with and preparing to teach Big Data: 

Follow the trendsetters. Some universities are taking a big leap forward into analytics, and faculty can gain valuable insight by researching these schools’ programs. Michigan State University and West Virginia University, for example, offer a Master of Science degree in Business Analytics. Faculty from numerous functional areas—accounting, finance, economics, supply chain management, marketing, and management information systems—helped create WVU’s program, which teaches students how to collect, analyze, and interpret data in an ethical and strategic fashion, said WVU accounting associate professor Ludwig Christian Schaupp, Ph.D.

The University of Mississippi offered a special session this past summer called “Data Analytics for Accountants,” and this spring accounting faculty plan to incorporate concepts from the session into their Accounting Systems Seminar.

Do your homework. Faculty can learn more about Big Data by “reading about the topic and engaging with practitioners who are dealing with the realities of Big Data every day,” Bryan said. Bryan suggested that faculty bring in guest speakers who can describe real-life scenarios. Academics can also partner with some of their colleagues in the computer science, data science, and statistics departments, or visit sites such as the AACSB, coursera.org, or any of the analytics sites run by the Big Four firms, such as Ernst & Young’s, to get the latest input on Big Data.

Study up. Coursera and Lynda.com offer online courses on Big Data for a fee, while Big Data University, an IBM initiative, and Teradata University Network provide them free. Faculty should also consider attending conferences and workshops that focus on business analytics. By attending these events, Schaupp noted, academics will stay current and be better able to provide a curriculum that addresses the growing data analytics field.

Familiarize yourself with new hardware and software tools. The tools required to “organize and work with the much larger data sets,” Wenger said, are becoming more approachable for nontechnical faculty. They include business analytics software Tableau, open-source predictive analytics platform Rapid Miner, Excel add-on Power Pivot, and Apache Hadoop, an open-source software framework for large data sets.

Spreadsheets are no longer enough to manage Big Data, Schaupp said. “Spreadsheets only allow you to analyze in the aggregate,” he explained. “It’s equivalent to using an X-ray when you need an MRI or genetic mapping.”

Start small. Big Data is a big subject, so teaching it on a level that is not overwhelming is key. “You have to teach the concepts of Big Data in a small context so that people can understand it,” Bryan said. “Whether you’re working with a thousand rows of data or a million rows of data, the methodology you teach should be the same.”

Incorporate real-life examples. Wenger’s students gather information from the U.S. Census, which provides free data sets that they can download and work with in his courses. They also “work with baseball statistics to get a feel for how to work with Big Data sets,” he said. Bryan, who previously taught undergraduates about accounting information systems, used case studies and simulations with his students.

Emphasize visualization and storytelling. Big Data makes more sense when students can see why it is actually important to an organization. Show students, for example, how accountants can use software “to analyze a Twitter feed for patterns in order to determine potential fraud scenarios,” Wenger said. Since text-analysis tools allow one to identify word patterns, keywords, and even the emotional tone of phrases in a large volume of messages, investigators can “evaluate tweets that mention a company during its quiet period to determine the tenor and sources of information as a first step in an insider trading investigation,” he added.

Stress that learning Big Data should not be optional. Many CPAs now need to be able to analyze huge volumes of both structured and unstructured data, put it into context, and use it to determine trends and discover theories. Businesses and agencies such as the SEC are jumping on the Big Data bandwagon because analyzing this information can help increase performance, productivity, and profits—and prevent security breaches and fraud.

“Big Data will not only give accounting professionals a seat at the table,” Schaupp said, “but will put them at the forefront in informing strategy.”

Cheryl Meyer is a California-based freelance writer.

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