Incorporating analytics into the tax curriculum

Editor: Annette Nellen, Esq., CPA, CGMA

A recent survey from Deloitte Insights reports that 90% of CEOs are aware that digital technologies are forcing disruptive change and 70% of those say that their employees do not have the skills to adapt (Pelster et al., “Careers and Learning: Real Time, All the Time” (Deloitte 2017), available at www2.deloitte.com. This is not surprising since Deloitte also reports that the half-life of a learned skill is only five years, while those working in finance and accounting must learn new skills every 12 to 18 months.

At the same time, the length of a ­career now ranges from 60 to 70 years, and the average tenure in each job is only four to five years (Gratton and Scott, The 100-Year Life: Living and Working in an Age of Longevity (Bloomsbury 2016)). Further, among Millennials, a key component of an employer’s brand in the job marketplace is “the ability to learn and progress” as the career itself — rather than just a formal education — becomes “a journey for learning” (Pelster et al., “Careers and Learning: Real Time, All the Time”). In fact, formal education is becoming less and less relevant — the Pearson Global Learner Survey from September 2019 (available at www.pearson.com reported that half of Generation Z respondents surveyed think that a college education is not necessary to succeed in life, as they are more reliant on self-learning.

The importance of adapting techniques to more efficiently and effectively train students to succeed in the workplace today cannot be overstated. In addition to persistent questions about the value of higher education, the world is moving into what many are referring to as the Fourth Industrial Revolution (see Mezzio, Stein, and Stein, “Robotic Process Automation for Tax,” 228-6 Journal of Accountancy 18 (December 2019), available at www.journalofaccountancy.com. While the Third Industrial Revolution refers to the digital revolution that started in the 1950s, the Fourth Industrial Revolution is seen as a “fusion of technologies that is blurring the lines between the physical, digital, and biological spheres” (Schwab, “The Fourth Industrial Revolution: What It Means, How to Respond” (World Economic Forum 2016), available at www.weforum.org. More than ever, emerging technology breakthroughs — which are now exponential rather than linear — will drive demand for employees who are skilled in those new technologies. Perhaps what is most frustrating to educators is that they do not yet know what those technologies are, so the task of adapting their teaching techniques seems overwhelming if not impossible.

However, adapting their teaching techniques does not necessarily require expertise in — or even knowledge of — those technologies of the future. What it does require is implementation of technology across the accounting and business curriculum (which is already needed for AACSB accreditation) and fostering a curiosity and desire for lifelong learning in students. In fact, for this fast-paced, ever-changing, technology-heavy environment, the World Economic Forum lists the top 10 skills needed for success as complex problem-solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgment and decision-making, service orientation, ­negotiation, and cognitive flexibility (Gray, “The 10 Skills You Need to Thrive in the Fourth Industrial Revolution” (World Economic Forum 2016), available at www.weforum.org. No one specific technology is listed here, and most educators would likely agree that these skills have been important for success in the workforce for decades.

This column discusses some techniques for developing these skills in a technology-heavy classroom environment, including tax courses as well as other accounting courses that students interested in pursuing tax will take as part of an accounting curriculum and a business degree. It also discusses the strategies being pursued by the Big Four firms to close this skills gap, and a sample of the resources available to educators. This column also shows that adapting teaching techniques and preparing students for the workplace of today and the future is not as daunting as it might seem. It does, however, require that educators work together to share resources with each other and partner with those in public accounting and industry to continue to adapt.

Analytics in the workplace

In this age of exponential growth in emerging technologies, two oft-repeated buzzwords in the accounting and business world are “big data” and “data analytics.” While the overuse of these words can make them seem meaningless, big data and data analytics have had a significant effect on the job tasks and types of jobs available for accountants and the services they provide to clients. Big data is defined in the dictionary as “extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions,” while data analytics is the analysis of these datasets using different technological tools to draw conclusions and make decisions. Andrew Bauer provides an excellent introduction and expansion of these concepts in a prior Campus to Clients column (Bauer, “Data Analytics: A High-Level Introduction for Accounting Practitioners,” 48 The Tax Adviser 366 (May 2017).

The importance of analyzing information and data to draw conclusions is the same as it has been for decades. In fact, it is the availability and accumulation of data (aka big data) that has had such a profound effect on the accounting profession, as traditional means of synthesizing and analyzing information are not sufficient for the volume of data. Clients now provide their accountants with access to a whole database of transactions that would easily crash a system like Excel, so software tools such as Tableau, Power BI, and Alteryx are now used to perform data synthesis and analysis. These are just three examples of new technology, and the technology used to perform this synthesis and analysis continues to evolve, and each new option provides yet more opportunity for analysis and insight. As a result, clients and their shareholders expect that accounting professionals will keep pace with emerging technologies to continually improve the services they offer.

Each of the Big Four firms, along with many large regional and national firms, has part of its practice dedicated to analytics. EY details how the integration of analytics within the tax function helps to manage risk, control cost, and drive business decisions (EY Americas, “How Data Analytics Is Transforming Tax Administration” (2019), available at www.ey.com. The IRS has been using analytics for years, but those analytics are now more robust and occurring in either real time or close to it — allowing the IRS to better target collection opportunities, more effectively increase compliance, and more efficiently collect revenue.

Recognizing that developing a pipeline of employees with a talent for analytics and artificial intelligence (AI) will be key to serving clients in this ­environment in the future, in March 2019, EY announced the launch of the NextWave Data Science Challenge. Targeting top data science students at hundreds of universities across 16 countries, this challenge requires students to use data from Skyhook to find solutions to issues surrounding mobility and smart cities (EY (press release), “EY Announces the Launch of a Global Data Science Challenge to Identify and Develop Top Talent in Analytics and Artificial Intelligence” (3/21/19), available at www.ey.com.

A recent survey report by Deloitte referred to tax analytics as “a new era for tax planning and compliance,” and though the tax profession has been late to adopt analytics (relative to other areas in accounting), it has quickly become an important element to increase efficiency and compliance as part of long-term business strategy (Deloitte, “Tax Analytics: A New Era for Tax Planning & Compliance” (2016), available at www2.deloitte.com. Respondents to the ­Deloitte survey report that tax analytics is a useful tool for daily recurring tasks such as understanding drivers of tax in key areas, predicting earnings and tax impacts including sales and use taxes, and making comparison between units over time as well as analysis of implications of selling assets and other major decisions.

Analytics is also useful for sampling tax items to better understand potential errors and audit risk, analyzing unstructured documentation, and interpreting tax law. To that end, Deloitte has partnered with the University of Illinois to establish the University of Illinois-Deloitte Foundation Center for Business Analytics, where a free course in the Foundations of Data Analytics (60 hours of content) is available to educators for use in their curriculum, along with three case studies that illustrate applications (University of Illinois-Deloitte Foundation Center for Business Analytics, available at www2.deloitte.com.

PwC groups tax analytics into five levels, including (1) tax business intelligence self-service; (2) dashboards, cockpits, scorecards, and visualization; (3) predictive analytics and tax planning; (4) prescriptive analytics, tax modeling, and decision automation; and (5) adaptive learning: data mining, AI, and machine learning. It has launched an online analytics academy and automation academy to take learners through the first three levels (see more at www.pwc.com. This academy offers online, self-paced training programs that can be used by business professionals or university faculty to better understand how analytics play a role in the tax function of the future. PwC has also partnered with Coursera to create a five-course series on data analysis and presentation skills, with a learning outcome to “use the data and analytics framework to develop a plan to solve a business problem” (see click.linksynergy.com).Enrollment is free, and the course takes approximately three months to complete.Those who take the course can also opt in to PwC’s talent network, which would mean they receive information about career opportunities and other things happening at PwC.

KPMG launched the Lighthouse — a “Global Center of Excellence for data-driven technology” — as a way to address client needs around emerging technologies and data analytics (see more at home.kpmg). And KPMG has taken its dedication to education in analytics to the next level by blending practice and academics through the creation of partnerships to sponsor master’s degrees. In 2017, it created partnerships with Villanova University and The Ohio State University to offer the KPMG Master of Accounting With Data and Analytics Program (Sheridan, “Big 4 Firms Bring Data Analytics to the Classroom,” AccountingWEB (Aug. 30, 2016), available at www.accountingweb.com. The partnership has now expanded into nine university programs that include paid tuition and room and board, a stipend, a spring semester internship, and a full-time experienced associate job with an accelerated career track at KPMG (KPMG, “Frequently Asked Questions: KPMG Master of Accounting With Data and Analytics Program” (May 2019), available at www.kpmgcampus.com. The intention of the program is to give incoming accounting professionals “the analytical skills and critical thinking to improve procedures and deliver insights that are expected in the data age.”

Analytics in the classroom

The clear and urgent need for accountants to be more skilled in data analytics and analytical thinking is well established. But how is this different from 10 or even 20 years ago? Has analytical thinking not been taught in classrooms for decades? For example, here are abstracts from two audit case studies published in accounting journals. Can you tell which of these two cases was published recently?

Case 1: In this simulation of a merger-and-acquisition due diligence engagement for a fast-fashion retailer’s inventory account, learners develop IT audit skills by (1) preparing a business process representation, (2) identifying audit objectives for testing management assertions about inventory, (3) designing audit procedures to implement audit objectives, (4) querying data files to execute audit procedures, and (5) communicating results. The simulation develops skills for analyzing data to verify the internal consistency of accounting records and to detect conditions warranting further investigation. The simulation, workable with a database query tool or audit software, is appropriate for students with querying proficiency and audit procedure design capability.

Case 2: This case presents an introduction to using computer-assisted auditing technologies (CAATs). Specifically, this case integrates ACL audit software into the financial statement auditing class. The client, Norwood Office Supplies Inc., operates in the highly competitive retail office supplies industry. In this assignment, you will be required to first identify business/operational and financial-reporting audit risks and then recommend audit procedures that can address such risks using the ACL software. You will then be asked to identify issues that may lead to opportunities for providing the client with different types of assurance and consulting services, and you are required to execute the audit procedures using ACL.

This was a trick question! The first abstract is from a 2008 paper in the Journal of Information Systems (­Borthick and Curtis, “Due Diligence on Fast-Fashion Inventory Through Data Querying,” 22-1 Journal of ­Information Systems 28 (March 2008)), while the second is from a 2001 Issues in Accounting Education paper (Gelinas et al., ­”Norwood Office Supplies, Inc.: A Teaching Case to Integrate Computer-Assisted Auditing Techniques Into the Auditing Course,” 16-4 Issues in Accounting Education 603 (November 2001)). Case studies and assignments using analytics software like ACL have been around for nearly 20 years. The first edition of a course pack “Data Analytics for Auditing Using ACL” was published in 2004 (Arens, Elder, and Borsum; see the Armond Dalton website at www.armonddalton.com. Training students to think critically and have an analytical mindset is not new, and audit educators have been using audit software to do this since the early 2000s.

Similarly, the underlying concepts of tax analytics as part of a tax curriculum are not new. Let us revisit the Deloitte survey results of things impacted by tax analytics: “Understanding drivers of tax in key areas; Predicting earnings, tax impacts, sales and use taxes; Making comparison between units over time; Analyzing implications decisions such as buying or selling assets; Sampling tax items to understand potential errors and audit risk; Analyzing unstructured documentation; Interpreting tax law” ­(Deloitte, “Tax Analytics: A New Era for Tax Planning and Compliance” (2016)).Can taxation faculty say that they were not using data and analytical thinking to teach these concepts over the past few decades? A review of a market-leading tax textbook published 10 years ago will yield multiple assessments and exercises where students are asked to perform these functions, though using Excel rather than Alteryx or Tableau to conduct the analysis (Pope, Anderson, and Kramer, Federal Taxation Comprehensive 2009 (Prentice Hall 2009)).

In fact, one significant adaptation in the classroom should be the use of new technologies to perform these analytical thinking tasks more efficiently. Courses in auditing and financial statement analysis are opportunities to integrate emerging technologies as a part of the analysis already completed in those courses. In these upper-level courses, educators can use new software tools to expose students to an array of options and be more efficient in analyses. Even small changes to an assignment can help a student further grasp the idea of an analytical mindset. For example, in one of the authors’ auditing courses, students perform a risk assessment of a company. In previous years, the assignment was presented as an overall analysis of risk, but this year students were specifically asked to perform a financial statement analysis, comparing year over year (and to a competitor), and to prepare and interpret visualizations in the presentation using either Excel or Tableau.

Another important adaptation in the curriculum should be to integrate the concept of analytical thinking earlier in a student’s academic career. It might have been acceptable 10 years ago to focus solely on content (such as journal entries) in the first financial accounting class, but that will not be enough to help students develop the analytical and technologically flexible mindset they need for today’s business world. Some accounting faculty teaching at the introductory level are using a familiar technology — Excel — to begin the discussion about and integration of analytical thinking into the accounting curriculum. For example, Wendy Tietz and Tracie Miller Nobles provide many examples of datasets and analytical thinking assignments in Excel to be used specifically in introductory accounting courses (Tietz and Nobles, “Data Analytics in Introductory Accounting” (2018 Honorable Mention for the Bea Sanders/AICPA Teaching Innovation Award)). The added benefit of integrating these assignments earlier in the coursework is to lay the groundwork for analytics and other analytical software to be introduced in later courses.

As mentioned above, tax faculty have been requiring students to use Excel to analyze trends in taxable income, tax paid, tax impact of decisions between alternatives, and depreciation across multiple years for multiple assets. Four recent Campus to Clients columns provide excellent examples of how taxation faculty can implement Excel into tax courses in a seamless fashion: Evans et al., “Using Excel in the Classroom: Performing a Multilevel Tax Analysis of an S Corporation Conversion,” 47 The Tax Adviser 350 (May 2016); Evans and Hansen, “Preparing the Income Tax Footnote: A Comprehensive Study in Excel,” 48 The Tax Adviser 826 (November 2017); Brink and Hansen, “Using Big Data to Identify Tax Risk,” 49 The Tax Adviser 318 (May 2018); and Brink et al., “Modeling ­Investment Tax Planning With Excel,” 49 The Tax Adviser 768 (November 2018).

Each of these cases will accomplish the objectives of teaching tax concepts, familiarizing students with Excel, and developing an analytical and technologically flexible mindset. Many tax faculty also require students to prepare electronic workpapers and use those to prepare a return by hand, or by using tax software, or both (see, e.g., Morrow and Stinson, “Mr. and Mrs. Smith: A Student Introduction to Federal Tax Compliance and Documentation,” 31-1 Issues in ­Accounting Education 12 (February 2016)). Though tax software is not technically “analytics,” learning the rules of the road and quirks of using a new piece of software requires a technologically flexible mindset, which is a valuable skill.

A final important adaptation to the curriculum is to introduce technology and software beyond just Excel. Students now are digital natives — the incoming freshman class this year was born in 2002 and does not know a world without computers, the internet, or even smartphones — and are comfortable using technology in all facets of their lives. The more educators can meet them where they are — online and on their phones — the more willing and eager they will be to learn and engage inside and outside of the classroom. Fortunately, resources are available to help facilitate this transition for faculty who are not sure where to begin.

Markus Aherns and Cathy J. Scott are accounting professors who started the blog Teaching and Learning Toolbox (teachingandlearningtoolbox.com) in July 2015 to share tips and tricks for using software, hardware, and apps to implement technology into the accounting classroom (in 2018, Aherns and Scott won the Bea Sanders/AICPA Teaching Innovation Award). The site is free for everyone and is educator-friendly — they even tie each tip to the applicable category of Bloom’s Taxonomy. A recent post gives advice on implementing data analytics across the accounting curriculum, including introducing data visualization using Power BI and Tableau and coding using The Hour of Code and Code Academy (Scott, “Integrating Data Analytics in Courses Across Your Curriculum to Raise the Learning Bar!” (July 31, 2019), available at teachingandlearningtoolbox.wordpress.com. Implementing just one or two of these technologies each semester could make a difference, not only in the level of engagement in your classroom, but also in the relevance of the tools for students’ future careers.

The HUB of Analytics Education (hubae.org) provides open educational resources to 499 universities in 41 countries. The HUB was co-founded by two accounting faculty who were frustrated with “small data” and wanted to create a big dataset for use in their classes so students would be required to move beyond Excel and use the tools Tableau, NetSuite, Qlik, and Idea to view and analyze data. Referring to themselves as “Big Data Pioneers,” volunteers at the HUB have a mission of “preparing students for the future, millions of records at a time” and set out to accomplish this mission through creating a series of datasets and corresponding exercises, process flowcharts, and process manuals for implementation into accounting courses. Detailed solutions manuals and a series of YouTube videos (youtube.com/hubofanalyticseducation) make implementation relatively painless for faculty, and the team at the HUB will also facilitate obtaining licenses for short-term use of NetSuite. A case specific to taxation is currently in development by the team at the HUB, but even the non—tax-specific cases could be implemented in a tax course to solve a business problem and demonstrate Tableau or NetSuite.

Partnering and sharing

Prior educational research has also shown that academic institutions have been slow to adapt to what the profession demands, creating significant gaps between what accounting programs expect from graduates and what companies expect from new hires (see Bolt-Lee and Foster, “The Core Competency Framework: A New Element in the Continuing Call for Accounting Education Change in the United States,” 12-1 Accounting Education 33 (2003); Jackling and De Lange, “Do Accounting Graduates’ Skills Meet the Expectations of Employers? A Matter of Convergence or Divergence,” 18 Accounting Education: An International Journal 369 (December 2009); and Yu and Churyk, “Are Students Ready for Their Future Accounting Careers? Insights From Observed Perception Gaps Among Employers, Interns, and Alumni,” 10 Global Perspectives on Accounting Education 1 (2013)). All report that employers believe accounting curricula do not adequately prepare accounting students for success in an accounting career. Thus, a body of accounting education research over the long term exposes an expectation gap, but no current research shows that these gaps have been closed.

Bauer recommends that practitioners and educators work together to establish a common set of tools that students would be expected to master upon completion of an accounting degree, which would include a set of courses including programming, algebra, and probabilities that would encourage students to think more like data scientists (Bauer, “Data Analytics: A High-Level Introduction for Accounting Practitioners,” 48 The Tax Adviser 366 (May 2017). And, as illustrated earlier, the Big Four have invested significant resources to ensure that tax faculty have the proper skills to teach analytics with an accounting or taxation application to help close these gaps.

By all appearances, the accounting academic community is working to close this gap. In 2018, the American ­Accounting Association (AAA) sponsored 22 section and regional meetings, and all but four of those meetings included a session on data analytics. An additional four-day conference on Intensive Data and Analytics was initiated in 2018. The Issues in Accounting Education 2019 Best Paper Award was given to Lauren M. Cunningham and Sarah E. Stein for “Using Visualization Software in the Audit of Revenue Transactions to Identify Anomalies” (see aaahq.org).

The American Taxation Association (ATA) has also partnered with firms to ensure tax faculty have proper skills, technology training, and data access to teach tax analytics in their tax courses. For example, the 2018 ATA Teaching and Learning Conference offered an intensive seminar sponsored by Deloitte, which provided data for classroom use, as well as hands-on sessions to demonstrate usage of Tableau to conduct analysis of datasets provided for classroom use. In 2019, the keynote for the same conference was a presentation by the HUB of Analytics Education on using Tableau and the HUB’s in-development tax case.

However, academic research in accounting education is a critical avenue for accounting faculty to exchange ideas and share best practices in the classroom. As faculty members develop new skill sets from the seminars and tutorials that are provided by firms, published research should be an avenue for sharing cases, teaching techniques, and experiences teaching tax analytics with the tools currently provided from the firms, as well as effectiveness of these experiences in the classroom. Analysis of the primary education journals shows a total of four instructional cases published in 2018 with a focus on data analytics, but unfortunately only one of them focused on tax analytics (Apostolou et al., “Accounting Education Literature Review (2018),” 47 Journal of Accounting Education 1 (June 2019)). In 2014, of 169 accounting education articles in the six recognized accounting education journals, only 4% (seven total articles) covered taxation. Of these seven, none of them had a foundation in analytics (Apostolou et al., “Accounting Education Literature Review (2013-2014),” 33-2 Journal of Accounting Education 69 (June 2015)).

For the academic community, this should be a call to action. Taxation faculty should explore publication opportunities to share with other faculty members what they are doing to teach analytics in the classroom, so faculty can learn from each other to better educate students. Faculty should also share what they are doing in the classroom with those in the profession. Firms can then better work with colleges and universities to ensure they have the tools needed to properly educate students. To date, those in the profession have shown that they are willing to advise and collaborate to ensure that what is being shared and taught is consistent with professional needs. This will only become more important as the skills and expectations gaps grow.

Adapting to the Fourth Industrial Revolution

The idea of analytics is not new. Educators have been teaching students to gather, analyze, and evaluate information to support a conclusion for decades. Critical thinking has long been a part of what it means to be an accountant. Being able to prepare a set of financial statements is a skill, but the higher-order skill of critical thinking is what sets a CPA apart. As long as educators have been training students to succeed in the profession, they have been training them to be analytical thinkers. As mentioned above, adapting teaching techniques does not necessarily require expertise in — or even knowledge of — those technologies of the future, but some adaptations are required to prepare students for successful careers in accounting and taxation.

The practice of higher-level thinking through analysis and conclusion has been more common in upper-level courses, as educators believed that students needed to have a foundation in accounting before they were able to come up with their best analysis. The first adaptation, then, is to implement analytical thinking early in the curriculum, integrating Excel problems using pivot tables and other means of analysis into introductory accounting courses. Another adaptation is adding more common, but still emerging technology tools, like Tableau, into upper-level classes to assist in a more efficient and effective analysis and evaluation of information. Creating technologically flexible thinkers can also be spurred by more implementation of technology in the classroom in general, which will create a more engaging environment overall for the digital natives being taught.

Firms are also doing an outstanding job offering educational opportunities to educators so appropriate tools are provided to teach analytics in taxation, but taxation faculty are not doing an adequate job with the production of educational research to share what they are doing, and they lag behind other areas of accounting education, such as auditing or financial accounting. Sharing these resources will remove some of the uncertainty (and impossibility) of implementing analytics in the classroom for accounting and taxation educators who have been reluctant to take that first step. This will bring educators one step closer to implementing technology across the accounting and business curriculum and fostering that curiosity and desire for lifelong learning in students.