Lessons learned: Incorporating data analytics in tax curricula.

• Author: Cheng, Christine

Data analytics is a valuable tool that is used to gain insights from ever-increasing volumes of data. To help students become familiar with how to apply this method of analysis to tax data, educators should teach data analytics in the classroom. They can do so by mirroring a process currently being used by entry-level accounting professionals called "extract, transform, and load" (ETL) and focusing on developing students' data analysis and visualization skills. These educational experiences will give students a better foundation for a successful career in tax, no matter what career path in the profession they may pursue.

While it is important to incorporate analytics into the tax curriculum, the implementation may seem daunting. To help, this column identifies ways that educators can overcome four major challenges: (1) What specific tools should be taught?; (2) What data analytics processes should be taught?; (3) How can data analytics be incorporated into a course that is already packed?; and (4) How can data analytics cases that are already available be adapted to meet specific needs?

Along the way, this column highlights new tax data analytics cases that can enhance students' learning experiences.

What specific tools should be taught?

Should you teach A2019, Access, Altcryx, Azure, Blue Prism, Java, C++, Excel, Gephi, Knime, Power BI, Python, R, SAS, SQL, Tableau, and/or UiPath? The anticlimactic answer is that the specific tool you choose to teach is less important than the data analytics process. However, before discussing the data analytics process, this column offers some practical advice for selecting data analytics tools.

First, teach the tools that are used by the firms that hire your students. Your program's advisory board members, firm recruiters, and recent graduates can provide valuable insight as to the needs of your particular job market. It may be useful to inquire if there are tooling transitions underway, particularly with the increasing use of cloud-based analytical applications.

Second, do not underestimate the importance of fundamental Excel skills. Excel remains a staple for employers, partially because clients, managers, and partners commonly feel comfortable with Excel. However, employer feedback continues to indicate that students' Excel skills fall short of expectations. Since the student population is diverse, ranging from tech-dependent to tech-sawy, instructors must be mindful of covering the basics (e.g., file management, copy and paste, formulas, cell labeling, formatting), in addition to identifying projects that allow students to develop advanced skills (e.g., VLOOKUP, conditional formatting, PivotTables, and visualizations). One case that may help with Excel skills is Christine Cheng, Pradcep Sapkota, and Amy J.N. Yurko's "A Case Study of Effective Tax Rates Using Data Analytics," a forthcoming article in Issues in Accounting Education and the winner of the 2019 ATA/Deloittc Teaching Innovation Award. The case focuses on developing students' data evaluation, PivotTable, and data visualization skills using Excel. The support material for this case includes custom instructional videos that address basic and more advanced Excel skills.

Educators who are ready to introduce a more powerful data analytics tool can quickly move students from Excel to Power BI, since the functions used in Power BI are similar to Excel's. Using an add-on approach (such as beginning with Excel and then introducing Power BI) can ensure that you educate students at all levels along the technology spectrum while helping students understand that they can adapt skills learned in one tool to other tools.

Third, while Excel and Power BI are powerful, these tools are not universally ideal for data analytics. In response to the need for more advanced tools, Alteryx (built around the ETL process) and Tableau (designed for effective data visualization) are increasing in importance in the accounting profession. Further, there is continued convergence among these tools in an effort to become the preferred data analytics tool (for example, Tableau now offers data transformation capabilities, and Alteryx now offers data visualization capabilities). Both programs offer cost-free licenses to students and faculty. The low-code characteristic of these tools is particularly appealing to students. Alteryx's repeatable workflow and ability to track data transformations throughout the ETL process is particularly appealing to employers. Alteryx or other Windows-based data analytics tools can be run on iOS-based operating systems if the student uses VMware, such as Boot Camp.

Lastly, instructors should consider incorporating at least an introduction to a code-based tool, for example, Python, R, or SAS. Code-based software programs are powerful tools that provide comprehensive data cleaning, manipulation, and analysis capabilities. Much of the stress can be reduced by providing students the appropriate code and incorporating class demonstrations. An introduction will not make students fluent in coding, but it may...