By now, surely all accounting department chairs and business school deans know that their curricula should, or rather must, include data analytics, possibly as a stand-alone course or at least as part of a set of lower- and upper-division courses. Accounting faculty members have been using in-class examples, homework assignments, case studies, and simulations in intermediate, advanced, and cost accounting classes to ensure that this topic is covered. To aid in this process, textbook publishers have introduced sets of questions and problems in their homework managers or test banks, entirely new modules, or separate simulation programs such as McGraw-Hill’s SIMnet.
Professional organizations such as IMA® (Institute of Management Accountants), the American Institute of Certified Public Accountants (AICPA), the Association to Advance Collegiate Schools of Business (AACSB), and the National Association of State Boards of Accountancy (NASBA) also emphasize that business and accounting graduates need to be better at understanding Big Data. A timeline of the last five years illustrates how the industry, as well as organizations involved in higher education, have increasingly focused on data (or business) analytics skills in accounting (see Table 1).
Table 1: Professional Organizations’ Increased Focus on Data Analytics in Accounting
| Year and Organization |
What Happened |
| 2017, NASBA | Indicated it's time for accounting programs to include data analytics in the curriculum. |
| 2018, AACSB |
Highlighted the importance of analytics for business graduates and adopted business accreditation standard 5, requiring curricula to include, among other things, “Data analytics, including, for example, statistical techniques, clustering, data management, modeling, analysis, text analysis, predictive analytics, learning systems, or visualization.” |
| 2018, AICPA |
Developed the Pre-Certification Core Competency Framework, distinguishing accounting, business, and professional competencies. Accounting competencies include risk assessment, analysis and management, measurement analysis and interpretation, reporting, research, system and process management, and technology and tools. |
| 2019, AICPA |
Added the term “data analytics” to the CPA Exam Blueprints for the Auditing and Attestation (AUD) and Business Environment and Concepts (BEC) sections of the Uniform CPA Exam. |
| 2019, IMA |
Published the IMA Management Accounting Competency Framework, which includes the Technology & Analytics competency comprising information systems, data governance, data analytics, and data visualization; added to Part 1 of the CMA® (Certified Management Accountant) exam effective 2020. |
| 2021, AICPA |
Announced plans to launch a new CPA exam (effective 2024) with three core parts (AUD, Financial Accounting [FAR], and Regulation [REG]) as mandatory and one “discipline” section (Business Analytics and Reporting [BAR], Information Systems and Controls [ISC], and Tax Compliance and Planning [TCP]), which will be the candidate’s choice. |
| 2021, IMA |
Issued the report Data Visualization, which highlighted that data visualization skills are in high demand. This report described two types of data visualization, exploratory and explanatory, that are both critical to be successful. |
According to the AICPA, Kenneth W. Boyd, and Robert Half, one reason for the increased focus on data analytics is employer demand and the desire of individuals as well as programs or colleges to stand out in a competitive marketplace. Educators want to ensure that their students are ready for accounting firms’ current and future needs, including compiling large data sets, extracting the information needed, and presenting results to various stakeholders.
Despite the increased focus on the term “data analytics” by employers, professional organizations, and educators, its definition and place within the accounting curriculum is not quite clear. The purpose of this article is, therefore, to clarify the meaning of data analytics in the context of an accounting professional and to provide a summary of critical data analytics skills for accountants. We also include suggestions on how accounting educators can prepare accounting students for careers that include evaluating large data sets with new technology.
What Is Data Analytics?
Analytics is defined as the method of logical analysis, while data is factual information, such as measurements or statistics, used as a basis for reasoning, discussion, or calculation. Data can be understood as an observable, factual, and captured trace left behind, and analytics is finding patterns in data to gain insights and predict the future (Jakob Svensson and Oriol Poveda Guillén, “What is Data and What Can it Be Used For? Key Questions in the Age of Burgeoning Data-essentialism,” Journal of Digital Social Research, November 9, 2020).
Furthermore, data analytics is a process of data transformation for decision making and problem solving. Software providers define data analytics as the science of extracting, analyzing, and understanding raw data to model and identify decision-making patterns (see Alteryx, Informatica, Tableau, and Tibco). While the above approaches apply to any data, accounting industry publications focus on using data analytics to evaluate a company’s operational and financial information through advanced analysis of a large volume of data (Leslie H. Blix, Mark A. Edmonds, and Kate B. Sorensen, “How well do audit textbooks currently integrate data analytics,” Journal of Accounting Education, June 2021; Amy Igou and Martin Coe, “Vistabeans coffee shop data analytics teaching case,” Journal of Accounting Education, September 2016, pp. 75-86).
Finally, the AICPA provided a detailed definition of audit data analytics as a science and art of pattern discovery conducted through analysis, modeling, and visualization of data across units, systems, and products through building statistical or other models. The focus on analytics in accounting and auditing using a wide range of data types subject to such analysis implies two separate elements: data and analytics. Such understanding is inclusive of various sizes, structures, and sources of data, while the analytics can be done manually or with the help of technology, automation, machine learning tools, and AI assisting professionals to make informed decisions.
What Technology Skills Do Accounting Graduates Need?
In accounting practice, data evolved from summarized accounting data before using computers through the development of storage and retention of complete transactions using computers, opening doors to greater insights (Miklos A. Vasarhelyi, Alexander Kogan, and Brad M. Tuttle, “Big Data in Accounting: An Overview,” Accounting Horizons, June 2015, pp. 381-396). The era of expansion in the global business environment came with data volume, velocity, and variety growth. Finally, in the wake of environmental and sustainability reporting, data points include financial and nonfinancial data. Moreover, the profession faces business Reporting 4.0, the application of technology intelligence with the ability for mass customization and reporting aligned with the needs of heterogeneous stakeholders and the multi-objective enterprise (Michael G. Alles, Jun Dai, and Miklos A. Vasarhelyi, “Reporting 4.0: Business Reporting for the Age of Mass Customization,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 1-15).
Currently, advanced computing and analytics skills involving external data, operational data, and modeling don’t seem to be mainstream. Yet accountants need to expand their data preparation and communication abilities for decision making and assurance (Gary Spraakman, Cristobal Sanchez-Rodriguez, and Carol Anne Tuck-Riggs, “Data analytics by management accountants,” Qualitative Research in Accounting & Management, January 2021, pp. 127-147).
The demand for data analytics knowledge in the accounting industry is also driven by an audit transformation that relies increasingly on computerized system reports (Blix, et al.). Jennifer M. Cainas, Wendy M. Tietz, and Tracie Miller-Nobles noted that new tools such as AI and robotic process automation (RPA) force the requirements of accounting graduates to possess skills in analyzing, interpreting, and decision making using those tools (“KAT Insurance: Data Analytics Cases for Introductory Accounting Using Excel, Power BI, and/or Tableau,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 77-85).
As shown in Table 1, accounting licensing associations in public and managerial accounting include elements of data analytics in certification exams (Kevin Dow, Norman Jacknis, and Marcia Watson, “A Framework and Resources to Create a Data Analytics-Infused Accounting Curriculum,” Issues in Accounting Education, January 2021, pp. 183-205), and the AICPA developed the Pre-Certification Core Competency Framework distinguishing accounting, business, and professional competencies:
- Accounting competencies: risk assessment, analysis and management, measurement analysis and interpretation, reporting, research, system and process management, and technology and tools.
- Business competencies: strategic perspective, global and industry perspectives, process and research management, governance perspective, and customer perspective.
- Professional competencies: ethical conduct, professional behavior, decision making, collaboration, leadership, communication, and project management.
Several academic and practitioner-oriented publications have dedicated articles to exploring what skills accountants and accounting graduates should possess. Technology expertise and data analysis rank within the top three skills in industry and accounting recruiters’ articles (see Accounting.com, American Institute of CPAs and Chartered Institute of Management Accountants, Meghan Gallagher and Veronica Beagle, Gleim, Indeed, and Robert Half). Other listed skills include adaptability and flexibility, critical thinking, communication, problem solving, attention to detail, organization, preparing and reporting on financial statements, time management, general and industry business knowledge, and service orientation (see Table 2).
Table 2: Needed Technology and Data Analytics Skills
| Skill and Comment/Finding |
Sources |
|
Skill set #1:
This is desired mostly by large accounting firms, but even data analytics programs do not cover this topic. |
Stanton Heister, Matthew Kaufman, and Kristi Yuthas, “Blockchain and the Future of Business Data Analytics,” Journal of Emerging Technologies in Accounting, May 2021, pp. 87-98; The future is calling: blockchain’s impact on the accounting profession |
|
Skill set #2:
Accountants must learn to identify and use appropriate tools to master data and its integrity by asking accuracy, validity, and consistency questions. |
Vernon J. Richardson and Marcia Weidenmier Watson, “Act or Be Acted Upon: Revolutionizing Accounting Curriculums with Data Analytics,” Accounting Horizons, June 2021, pp. 129-144 |
|
Skill set #3:
Accountants can use data to develop an integrated and complementary analytical ecosystem for transaction monitoring and to identify exceptions. |
Miklos A. Vasarhelyi, Alexander Kogan, and Brad M. Tuttle, “Big Data in Accounting: An Overview,” Accounting Horizons, June 2015, pp. 381-396 |
Teaching Data Analytics in the Accounting Curriculum
How should educators teach data analytics in accounting? Considering the pressure to include a myriad of other traditional and novel topics—such as basic bookkeeping, adjusting entries, the accounting framework, business ethics, sustainability, corporate governance, international accounting (International Financial Reporting Standards) and tax, oral and written communication, and so on—how can instructors find time during traditional classroom lectures to improve accounting students’ grasp of the methods available to examine large sets of accounting and business data?
Some are asking for a complete overhaul of the accounting curriculum and even eliminating accounting departments (ICYMI: The Accounting Curriculum Needs a Complete Overhaul; The CPA Evolution Project Realigns the Professional Certification and Challenges Accounting’s Viability as a Stand-alone Major). Of course, current accounting graduates do need to know more than debits and credits and T-accounts. Without understanding the fundamentals, being proficient in IT doesn’t get you very far. We believe it’s necessary to combine both, learning how to record and interpret accounting data while at the same time applying IT programs to compile, visualize, and evaluate this information.
Many academic articles favor the approach to data concepts infusion across the curriculum (Dow et al.; Ralph S. Polimeni and Jacqueline A. Burke, “Integrating Emerging Accounting Digital Technologies and Analytics into an Undergraduate Accounting Curriculum—A Case Study,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 159-173; Amer Qasim and Faten F. Kharbat, “Blockchain Technology, Business Data Analytics, and Artificial Intelligence: Use in the Accounting Profession and Ideas for Inclusion into the Accounting Curriculum,” Journal of Emerging Technologies in Accounting, Spring 2020, pp. 107-117). Regardless of the approach, students must understand the foundations of working with data. Blix et al. recommended that auditing and accounting textbooks include a separate chapter describing data analytics foundations. Also, the Five Steps Data Preparation Techniques posted in the AICPA’s blog can help students understand the logic behind exercises. These steps include (1) plan, (2) access and prepare, (3) consider reliability and relevance, (4) perform, and (5) evaluate and conclude. Such coverage would help students understand how to use data analytics and its impact. Accounting educators can also choose from a few textbooks and many published teaching cases to accommodate teaching methods. Textbook publishers differ in teaching approaches and the tools available to educators and students. Educators can also use data analytics teaching cases published since 2020 in the IMA Educational Case Journal (IECJ®), Issues in Accounting Education, Journal of Accounting Education, and Journal of Emerging Technologies in Accounting. Note that IECJ includes a vast database of accounting cases using Excel.
We identified 35 teaching cases addressing at least one of the skill sets in Table 2. The appendix includes a full listing of the selected cases, including our judgment about applicability to course levels (junior, senior, Master of Accountancy, and MBA). Note that our analysis does not identify all teaching materials covering data analytics in accounting. We encourage educators to explore additional resources, including the Academic Resource Database by the AICPA, Deloitte’s Trueblood cases, Harvard Business Publishing Education cases, Rutgers Accounting Digital Library, FutureLearn, DataCamp, and Coursera.
Independently created assignments may also include behavioral learning aspects. William B. Mesa explored students’ behavioral cues in learning data analytics and recommended allowing failure and additional experimentation, aiming for optimal feedback; sensemaking behaviors of filtering, traversing, and prioritizing; and guidance in finding outcomes (“Accounting students’ learning processes in analytics: A sensemaking perspective,” Journal of Accounting Education, September 2019, pp. 50-68). Faculty can introduce these skills through scenarios, projects, examples, open-ended questions, frameworks, case studies, summaries, use of group/team projects with a presentation at the end, use of large data sets, missing data exercises, explanations, data storytelling, summaries, written documentation explaining what was done to various audiences and then present orally, factor analysis, ethics, and discussion (Teaching non-technical skills in the data-analytics program in higher education).
Textbooks, case studies, and many academic studies indicate that the industry is working on developing the necessary teaching tools. Does this mean that we have sufficient and the “right” educational resources available? Robyn L. Raschke and Kimberly F. Charron analyzed data analytics case studies and concluded that most cases focus on analysis and communication but don’t sufficiently cover the pre-analysis skills of data extraction and preparation (“Review of Data Analytic Teaching Cases, Have We Covered Enough? Journal of Emerging Technologies in Accounting, Fall 2021, pp. 247-255). Educators may consider modifying existing cases to incorporate these topics.
Data analysis in accounting is not a new concept, but the technological advancement in the last few years is driving an evolution within the industry, requiring a transformation within accounting education. Because employers wish to hire accounting graduates who have been exposed to various data analytics skills, students will seek programs with a focus on technology, providing the competitive edge for a long-term career. A fast-changing environment requires agility, and the best way to prepare graduates for the future is to embrace technology aptitude across the curriculum. Reimagined accounting curricula should start by designing courses that can easily be adapted to new technologies. Not every educator needs to be a technology expert, but we need to become experts in finding resources and tools to benefit graduates and assure the sustainability of our accounting programs.
Appendix: Selected Data Analytics Teaching Cases Mapped to Table 2 Skill Sets
| Articles and Resources |
Table 2 Skill Set(s) and Course Level(s) |
|
Lindsey M. Andiola, Denise Hanes Downey, Christine E. Earley, and Devon Jefferson, “Wealthy Watches Inc.: The Substantive Testing of Accounts Receivable in the Evolving Audit Environment,” Issues in Accounting Education, May 2022, pp. 37-51. Students use Interactive Data Extraction and Analysis (IDEA) software and RPA to evaluate client data, select samples, obtain evidence, identify exceptions, and project misstatements. |
2, 3 Senior and Master of Accountancy |
|
Ramji Balakrishnan and Claire Quinto, “Denim Products Incorporated (B): Profit Variance Analysis,” IECJ, September 2022. Students complete and interpret profit variance analysis using Excel pivot tables and charts. The visualization can be completed in Excel or Tableau. |
2 Senior, Master of Accountancy, and MBA |
|
Zeshawn Beg, “Flex Budgets and Variances at Speedy Tire Co.,” IECJ, Fall 2020. Students explore the income shortfall through variance analysis and information from various managers. Students complete the analysis in Excel, and the analysis is visualization-ready for charts and graphs. |
2, 3 Senior, Master of Accountancy, and MBA |
|
A. Faye Borthick and Lucia N. Smeal, “Data Analytics in Tax Research: Analyzing Worker Agreements and Compensation Data to Distinguish Between Independent Contractors and Employees Using IRS Factors,” Issues in Accounting Education, August 2020, pp. 1-23. Students prepare data analytics reports in Excel, identifying risky employment practices, estimating penalties, and recommending corrections. |
2, 3 |
|
Michael Burkert, Thomas G. Calderon, James W. Hesford, and Michael J. Turner, “Azure Lodging, Inc.: A Case Study on Capital Budgeting with Capital Rationing in a Service Industry Context,” Issues in Accounting Education, May 2022, pp. 67-89. Students are exposed to the advanced analytics and Monte Carlo analysis used for assessing uncertainty and use the analysis to develop cash flow projections for a hotel. |
2, 3 Senior and Master of Accountancy |
|
Jennifer M. Cainas, Wendy M. Tietz, and Tracie Miller-Nobles, “KAT Insurance: Data Analytics Cases for Introductory Accounting Using Excel, Power BI, and/or Tableau,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 77-85. Two KAT case studies introducing data cleansing, visualization, and insight found them relevant to students’ needs. Following the AACSB’s Standard 5, the cases used three types of technology, teaching students agility. The KAT cases were designed to meet the AICPA Pre-Certification Core Competencies of accounting (measurement analysis and interpretations, technology and tools), business (various perspectives), and professional competencies (decision making, communication). |
2 Junior, senior, and MBA |
|
Christine Cheng and Anu Varadharajan, “Using Data Analytics to Evaluate Policy Implications of Migration Patterns: Application for Analytics, AIS, and Tax Classes,” Issues in Accounting Education, May 2021, pp. 111-128. Students are exposed to Internal Revenue Service Statistics of Income migration data to identify policy impacts of local government. Students use Alteryx to clean data and Tableau for visualizations. |
2, 3 Senior and Master of Accountancy |
|
Christine Cheng, Pradeep Sapkota, and Amy J. Yurko, “A Case Study of Effective Tax Rates Using Data Analytics,” Issues in Accounting Education, February 2021, pp. 65-89. A case facilitating the use of Excel, Tableau, Alteryx, Compustat, and public data. Students analyze effective tax rates and influencing factors (economic, industry, and firm-level), and practice data analytics and visualization. |
2, 3 |
|
Madeline A. Domino, Daniel Schrag, Mariah Webinger, and Carmelita Troy, “Linking data analytics to real-world business issues: The power of the pivot table,” Journal of Accounting Education, December 2021. A case exposing students to summarizing large data sets, identifying data parameters to prepare pivot tables addressing management concerns, communicating findings and recommendations, and preparing visualizations in Tableau. |
2 Senior and Master of Accountancy |
|
Ning Du, Tawei Wang, and O. Roy Whittington, “Accounting Data Analytics Exercise for Intermediate Accounting: Warranty Expense and Product Liability,” Journal of Emerging Technologies in Accounting, Fall 2021, pp. 201-208. Students are provided with 14 years of sale and warranty expense data for 16 cars in five geographical regions; summarize data in pivot tables; prepare charts and graphs; analyze trends; and assess warranty expense reasonableness. |
2 Senior and MBA |
|
Nicholas J. Fessler and Christine A. Denison, “Travel-Space Trailers: A Budgeting Experience,” IECJ, March 2021. Students prepare budgets for multiple scenarios considering various business functions and policies. The case culminates with written business communication, including visualization through charts and graphs. |
2 Senior, Master of Accountancy, and MBA |
| Ingrid E. Fisher, Mark E. Hughes, and Diane J. Janvrin, “Put Your Best Text Forward: Introducing Textual Analysis into the Accounting Classroom,” Issues in Accounting Education, February 2022, pp. 141-195. Students are introduced to textual analysis methods using freely available software and U.S. Securities & Exchange Commission (SEC) filings. |
2, 3 Senior and Master of Accountancy |
|
Sonia Gantman and Lorrie Metzger, “Vendor Master Data Cleaning—A Project for Accounting Class,” Journal of Emerging Technologies in Accounting, Spring 2022, pp. 165-171. This case introduces students to planning and performing data cleaning of approximately 29,000 vendor records and concludes with two separate sets of deliverables. The solutions are created for the use of Excel and Alteryx Designer, but the case could also be applied to other data analytics software. |
2 Junior, senior, Master of Accountancy, and MBA
|
|
Donald Gribbin and Jagjit S. Saini, “GoGreen Supercenter: Energy Savings and Parking Lot Lighting System Case,” IECJ, June 2021. A case introducing students to capital budgeting and sustainability concepts. Students recommend and justify the choice of the lighting system for the supercenter, improve data analytics skills in Excel, and incorporate qualitative analysis (illumination, corporate social responsibility, sustainable alternatives, safety, disposal, and weather patterns). |
2, 3 Senior, Master of Accountancy, and MBA |
|
Stanton Heister, Matthew Kaufman, and Kristi Yuthas, “Blockchain and the Future of Business Data Analytics,” Journal of Emerging Technologies in Accounting, May 2021, pp. 87-98. A set of pen and pencil exercises introduces students to blockchain technology and ledgers. |
1 Junior, Senior, Master of Accountancy, and MBA |
|
Jamie L. Hoelscher and Trevor Shonhiwa, “Not So Fuzzy Auditing Analytics,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 99-112. This case exposes students to textual analytics. Students examine data sets for fraud and fictitious vendors using conditional formatting and a fuzzy lookup tool. |
2 Senior and Master of Accountancy |
|
Matthew Holt and Bradley Lang, “GADGET: An Accounting Data Generator,” Journal of Emerging Technologies in Accounting, Spring 2021, pp. 113-129. The article describes the Grand Accounting Data Generating Accounting Tool (GADGET) that faculty can use to create revenue transaction data sets. The tool is free, and data sets can be customized. An example of the turnkey student project focuses on building relational databases and queries and preparing visualizations. |
2 Senior and Master of Accountancy
|
|
Kara Hunter, Cristina T. Alberti, Scott R. Boss, and Jay C. Thibodeau, “IntelliClean: A Teaching Case Designed to Integrate Data Cleaning and Spreadsheet Skills into the Audit Curriculum,” Journal of Emerging Technologies in Accounting, Fall 2020, pp. 17-24. A case utilizing electronic spreadsheet platforms to teach students data cleaning and verification of completeness and accuracy. |
2 Senior and Master of Accountancy |
|
Stacie K. Laplante and Mary E. Vernon, “Incorporating Data Analytics in a Technical Tax Setting: A Case Using Excel and Tableau to Examine a Firm’s Schedule M-3 and Tax Risk,” Issues in Accounting Education, May 2021, pp. 129-139. Students apply corporate tax and income tax accounting concepts, complete tax work papers, and use descriptive data analysis and benchmarking. |
2, 3 Senior and Master of Accountancy
|
|
James G. Lawson and Daniel A. Street, “Detecting dirty data using SQL: Rigorous house insurance case,” Journal of Accounting Education, June 2021. Students learn the importance of clean and reliable data by analyzing claims insurance data, identifying issues, and proposing remediations. Students look for claims outside of reasonable range and payments over the insurance policy. Students are guided through data design and control, data dictionaries, steps in detecting dirty data, and queries. |
2 Senior and Master of Accountancy |
|
Lorraine S. Lee, Victoria Hansen, and William Brink, “Tax Retirement Savings Decisions Using an Excel Spreadsheet Approach,” Issues in Accounting Education, August 2020, pp. 39-55. Resources: Students use advanced Excel skills to calculate the tax impact of retirement investing. |
2, 3 Senior and Master of Accountancy |
|
Lorraine S. Lee, Deniz Appelbaum, and Richard D. Mautz III, “Blockchains: An Experiential Accounting Learning Activity,” Journal of Emerging Technologies in Accounting, Spring 2022, pp. 181-197. Based on the experiential learning approach of “Do, Reflect, Think, and Apply,” students learn about bitcoins, blockchain, and bitcoin blockchains; complete activity in a peer-to-peer network; track transactions through distributed ledgers; complete proof-of-work activities; and analyze the potential uses of the technology. |
1 Junior, Senior, Master of Accountancy, and MBA |
|
Mary M. Maloney, Stephanie D. Grimm, and Regina Anctil, “Atlas international business case: Examining globalization and economic indicators for the scrap metal recycling industry,” Journal of Accounting Education, June 2020. Students are exposed to financial and nonfinancial data while researching likely future prices to be offered to the Atlas company in export markets. Students need to understand the company’s issues, research the global marketplace industry, and access and use various publicly available data sets. |
2, 3 Senior, Master of Accountancy, and MBA |
|
Ann D. O’Brien and Dan Stone, “Yes, You Can Import, Analyze, and Create Dashboards and Storyboards in Tableau! The GBI Case,” Journal of Emerging Technologies in Accounting, March 2020, pp. 21-31. A case designed to provide students with firsthand experience of understanding, performing, and communicating the results of their data analysis. Specifically, students import data, clean the data, run queries, and prepare dashboards and storyboards. |
2 Senior, Master of Accountancy, and MBA |
|
Ann O’Brien and Dan N. Stone, “A Case Study in Managing the Analytics ‘Iceberg’: Data Cleaning and Management using Alteryx,” Journal of Emerging Technologies in Accounting, Fall 2021, pp. 221-245. |
2 Junior, Senior, Master of Accountancy, and MBA |
|
Daniel E. O’Leary, “Purchase Order ‘Analytic Audit,’” Journal of Emerging Technologies in Accounting, Spring 2022, pp. 199-211. Students are provided with a data set of approximately 13,500 purchase orders and asked to use multiple analytical approaches to identify exceptions, prepare time-series analysis, and prepare visualization of trends, significant items, and concentrations. |
2 Senior and Master of Accountancy |
|
Michael A. Robinson, Martin Stuebs, Lauren Wilfong, and Haylee Beard, “The cost to bus go round and round: Charting a route to a decision,” Journal of Accounting Education, March 2020. Students apply data analytics skills in Excel to evaluate outsourcing decisions in a school’s educational operations, prepare capital budgeting analysis, and consider ethics. |
2 Junior, Senior, and MBA |
|
Nikki Schönfeldt and Jacqueline L. Birt, “ICT Skill Development Using Excel, Xero, and Tableau,” Journal of Emerging Technologies in Accounting, Fall 2020. A comprehensive assignment exposing students to spreadsheet skills in Excel, Xero accounting cloud-based software, and visualizations in Tableau. |
2 Junior, Senior, Master of Accountancy, and MBA |
|
Karen Schuele and Elizabeth Felski, “Comprehensive Data Analytics Project Using Excel and Tableau for the Sales and Purchases Cycles,” Journal of Emerging Technologies in Accounting, Fall 2021, pp. 257-268. Students apply data analytics framework (ask the right question, ETL, analyze, and present results) using Excel for the sales cycle and Tableau for the purchase cycle. Project activities are mapped to EY’s Analytics Competency Framework. |
2 Junior, Senior, Master of Accountancy, and MBA |
|
Tara J. Shawver and Todd A. Shawver, “Teaching Data Analytics in a Collaborative Team Environment,” Journal of Emerging Technologies in Accounting, Fall 2020, pp. 57-62. This is a case teaching students data analytics in Excel and encouraging the use of Tableau for visualizations in dashboards. Students work with data of sales orders, approved price listing, and inventory cost; identify issues; discuss solutions to problems; and identify internal controls policies needed. |
2 Senior, Master of Accountancy, and MBA |
|
Ferdinand Siagian and Steven Johnson, “ATS Sports: Analysis of Managers’ Bonuses,” IECJ, June 2022. Students are exposed to performance evaluation using flexible budgets and variance analysis in Excel, followed by the visualization in Tableau for the purpose of bonus decision making. |
2 |
|
Harshini P. Siriwardane and Karen De Meyst, “Duralock: Budgeting for Decision Making,” IECJ, January 2022. Students prepare sensitivity analysis and dynamic budget components for a company in a fluid global environment and various scenarios. Students use Excel to conduct analytics for decision making. |
2 Senior, Master of Accountancy, and MBA |
|
Deb Sledgianowski, Steven T. Petra, Alexander Pelaez, and Jianbing Zhu, “Using Tableau to Analyze the Effects of Tax Code Changes: A Teaching Case for Tax and AIS Courses,” Issues in Accounting Education, August 2021, pp. 117-133. Students use large data sets of simulated individual tax returns and identify tax changes on individual taxpayers. |
2, 3 Senior and Master of Accountancy |
|
Theophanis C. Stratopoulos, “Teaching Blockchain to Accounting Students,” Journal of Emerging Technologies in Accounting, Fall 2020, pp. 63-74. Stratopoulos shares his approach to teaching blockchain through storytelling and scaffolding, outlining topics, discussions, and assignments. Students are exposed to foundational knowledge, hashing and proof-of-work, and implementation in supply chains. |
1 Master of Accountancy and MBA |
|
Amanuel F. Tadesse and Nishani Edirisinghe Vincent, “Combining Data Analytics with XBRL: The ViewDrive Case,” Issues in Accounting Education, February 2022, pp. 197-215. Students engage in data analytics using multiple data sets from eXtensible Business Reporting Language (XBRL), recommend financials preparation software adopting XBRL for SEC filings, perform data cleaning using ETL procedures, and generate visualizations. |
2, 3 Senior and Master of Accountancy |
