Deep Dive 2018: A Survey of Data Analytics Course Requirements in

the Undergraduate Accounting Curricula

Abstract

Teaching critical thinking. Listening to accounting practitioners. Going beyond Excel.

Encouraging students to see the bigger picture behind the technology. Ovaska-Few (2017) identifies

these four practices that can be adopted by accounting faculty seeking to introduce data analytics to their

students. What has raised the profile of data analytics over the last few years? A merging of several

global trends has made it possible to work with and scrutinize large sets of data. Additionally, accounting

and finance leaders in public practice, industry, and government have witnessed the benefits of bringing

data analytics to areas such as compliance and risk management, internal and external auditing, financial

statement preparation, and fraud identification.

But where does the undergraduate accounting curriculum currently stand in terms of introducing

students to and giving them practice working with data analytics (DA)? This project investigates the

question with a survey of 176 undergraduate accounting programs sampled from all 50 states. The

results? Sixteen percent of programs either require accounting majors to take at least one course in data

analytics or offer them as electives in the accounting major or business core.

After looking at the survey data, the paper identifies essential data analytics-related skill sets

needed in the workplace. It concludes with suggestions of ways accounting faculty can prepare students

to enter a workplace data analytics-aware.

Copyright Grace F. Johnson 2019. All rights reserved. 1

Deep Dive 2018: A Survey of Data Analytics Course Requirements in

the Undergraduate Accounting Curricula

Introduction

Teaching critical thinking. Listening to accounting practitioners. Going beyond Excel.

Encouraging students to see the bigger picture behind the technology. Ovaska-Few (2017) identifies

these four practices that can be adopted by accounting faculty seeking to introduce data analytics to their

students. What has raised the profile of data analytics over the last few years? A merging of several

global trends has made it possible to work with and scrutinize large sets of data. Additionally, accounting

and finance leaders in public practice, industry, and government have witnessed the benefits of bringing

data analytics to areas such as compliance and risk management, internal and external auditing, financial

statement preparation, and fraud identification.

But where does the undergraduate accounting curriculum currently stand in terms of introducing

students to and giving them practice working with data analytics (DA)? This project investigates the

question with a survey of 176 undergraduate accounting programs sampled from all 50 states. The

results? Sixteen percent of programs either require accounting majors to take at least one course in data

analytics or offer them as electives in the accounting major or business core.

After looking at the survey data, the paper identifies essential data analytics-related skill sets

needed in the workplace. It concludes with suggestions of ways accounting faculty can prepare students

to enter a workplace data analytics-aware.

Why Now?

Pan and Sun (2015) report that data analytics is advancing in importance in the field of

accounting for three reasons. First, tools used in analytics are more powerful and sophisticated. Second,

the unstructured and structured data used in DA has grown in volume. Finally, businesspeople have

become more focused on basing decisions on data, creating a “data-driven mind-set”. They note that in a

“data-driven decision making culture, there is opportunity for accountants to move beyond optimising the

accounting function to transforming the enterprise.” These three situations are explored in more detail.

Tools Used in Analytics are More Powerful and Sophisticated

Some data analytics tools have been around for a while, including the capabilities of Microsoft

Excel and Access. Analytical features of enterprise resource planning (ERP) software, including those

from Oracle and SAP, also have grown in complexity and are used. Other, newer DA-specific

applications such as ACL, BusinessObjects, Cognos, Lavastorm, MicroStrategy, or Tableau are

specifically designed for analytical work and offer tools targeted to deeply mine data (Ye, 2015).

Global public accounting firm and business consultancy EY, in a survey of more than 740

business executives around the globe conducted in October and November 2017, states three aspects of

data analytics technology have led to its more widespread use: data visualization, social media analytics,

and statistical analysis (EY, 2018).

One feature of data analytics software receiving a lot of attention is visualization. Oracle

describes visualization as “the presentation of abstract information in graphical form. Data visualization

allows us to spot patterns, trends, and correlations that otherwise might go unnoticed in traditional

reports, tables, or spreadsheets” (Oracle, 2015). Morgan (2015) explains that data users have led the call

for more interactive experiences with business intelligence and data analytics. She writes, “to facilitate

the democratization of data and accommodate the increasing volume, velocity, and variety of data,

Copyright Grace F. Johnson 2019. All rights reserved. 2

visualizations have improved aesthetically and functionally on a number of levels.” Visualization has

been credited with being a “game changer” for users because of its features allowing users to choose how

they see their data, choose their data, and drill down to data details quickly (Brands and Holtzblatt, 2015).

EY (2018) comments on the advantages brought by data visualization, including its “intuitive, audience-

appropriate reporting views and interactive capabilities to aggregate or isolate risk hot spots”.

Using data analytics to continuously monitor business transactions has become common in large

organizations. Particularly in the areas of risk management and compliance, DA techniques such as real-

time examination of all transactions can speed up discovery of fraudulent or inappropriate activity by

comparing each transaction to typical patterns of data behavior. An EY partner in the Fraud Investigation

& Dispute Services practice says, “data analytics will drive compliance functions toward the most

significant risk areas, unearthing important issues sooner and moving the focus onto ethics and integrity

management” (EY, 2018). In the auditing domain, statistical sampling emerged many years ago as an

important component because the tools to sort through business transactions did not exist. That has

changed with the advent of “automated audit software capable of testing datasets rapidly with minimal

manual involvement from the auditor” (Kogan, 2017).

To work with the extensive data sets used to support complex merger and divestiture decision

making, KPMG’s Deal Advisory and Strategy Group developed data analytics software KART. The

KPMG Aggregation and Reporting Tool (KART) “extracts from multiple, incompatible sources and

platforms” to enable “smoother, more transparent and more accurate” procedures required to advise its

clients (KPMG, 2018). Another example of data analytics software designed to work with global sets of

internal and externally generated data is the firm’s industry benchmarking software, Benchmarking Plus.

It was created to mine data from “KPMG’s robust, proprietary database which gleans information from

our engagements with 1,000+ private companies and large volumes of third party data” and supply

benchmarking KPIs for clients contemplating M&A activity (KPMG, 2018).

Unstructured and Structured Data Used in Data Analytics Has Grown in Volume

Remember a time when the phase “big data” referred to data characterized by its tremendous

volume, velocity, and variety (the “three Vs”)? These three characteristics haven’t changed since “big

data” become a hot phrase, but the extent to which such data is available has. There is more data, coming

at business users faster, and in formats and from sources that weren’t considered mainstream even a

couple of years ago. Brands and Holtzblatt (2015) identify tweets, maps, and location-identifying

applications as some of these newer types of data. Quality control personnel can glean useful information

by scanning voice and text from customer phone calls, e-mail, and customer service chat sessions; staff

working in social responsibility or corporate governance divisions would be interested in customers’

perspectives from their posts on social media (Richins et al., 2017).

A PwC report from 2015, Data Driven: What Students Need to Succeed in a Rapidly Changing

Business World, explains the business benefits of unlocking and connecting data typically hiding in data

warehouses. “In the accounting, tax, and consulting professions, we’re in the epicenter of the data

explosion, surrounded by information that exists quietly in disparate systems, often unused until it’s

pulled for an audit, engagement, or tax return. But if we turn reams of data into meaningful insights,

organizations can use these findings to improve their businesses in many functions—and accounting

professionals would add even greater value.”

One of the panelists on a Deloitte Dbriefs webcast on March 21, 2018, shared comments from C-

suite executives, saying, “all of a sudden, data that was buried in a grave somewhere is coming to life. We

have to make sense of it and use it as an asset to serve clients better” (Deloitte, 2018). Similarly, the chief

financial officer of Royal Dutch Shell emphasizes how the quantity and availability of data has grown,

even over the past few years. “Tapping into these information sources to react in tougher times is critical,

Copyright Grace F. Johnson 2019. All rights reserved. 3

and can be a major opportunity to create or project value” (CGMA, 2016). Such value creation is evident

when you consider fraud detection and risk management activities, where using data analytics techniques

to examine every transaction offers “a much better chance to identify trends and outliers that can guide

them [internal auditors] to potentially fraudulent activity or other problematic transactions” (Stippich and

Petrich, 2016).

Managing “ever-expanding volumes of information” is a strategic problem identified by 300 C-

suite executives from 16 countries interviewed in 2015 (CGMA, 2016). Boyd, Houk, and Miller (2017),

in an essay looking at the federal government’s use of data analytics, observe that “the amount of data

available to CFOs to analyze, and the imperative to analyze it, is growing exponentially”. Finally, it is

important to recognize that even while decisions are being made new data is continually available.

Yahoo’s CFO says, “modelling can be very dynamic in today’s world – the information is constantly

changing in a climate or environment that is more volatile than in the past” (CGMA, 2016).

The Data-Driven Mindset

The Merriam-Webster Dictionary1 defines the word “mind-set” as follows: a mental attitude or

inclination; a fixed state of mind; a particular way of thinking; a mental inclination, tendency, or habit.

Thus, all of the talk – and perhaps hype – over the data-driven mindset amounts to not much more than

altering our perspective about data and how we use it. But changing habits of the mind is a more difficult

task. With respect to data analytics’ use in business, the data-driven mindset must be balanced with

human experience, intuition, and reason. As John Botti states in an Independent School interview, “There

are ways that data thickens the conversation. Just as quantitative data shouldn’t stand on its own, neither

should qualitative reportage” (Khan and Vasu, 2018). Lyytinen and Grover (2017) write, “managers today

do not need to think in terms of optimization but rather in terms of how to use data to add value”. They

argue that an analytic mindset is essential for dealing with “open, emergent and innovative decisions that

now deeply affect corporate value creation”.

Emergent situations, complexity, randomness, experimentation, and data openness characterize

our business decision making environment, say Lyytinen and Grover (2017), requiring managers to apply

an “analytic mindset” to make the most of the data to which they have access. Papandrea (2017) explains

that one sign of a data-driven mindset is searching the data to answer specific questions, rather than

merely scanning data to see what happened. The CGMA (2016) stresses the importance of “prioritizing”

data and understanding the right time and purpose for using data to support decision making.

Data analytics tools offer aid in making operational and long-range choices. The information

technology controller at Cummins, Inc. observes that his company’s executives turn to their management

accountants as business advisors and partners. Understanding data analytics helps “in interpreting and

utilizing” data (Brands and Holtzblatt, 2015). An executive director at accounting and finance recruiter

Robert Half notes that data analytics skills are now “mandatory” because of significant shifts toward data-

driven decision making (Brands and Holtzblatt, 2015).

Predictive analytics, the use of data analytics to picture the future from past data, has the capacity

to alter how businesses undertake forecasting tasks, ranging from creating annual budgets to determining

strategic M&A activity. Adopting a predictive analytics approach will demand “a change in thinking at

all management levels” write Stippich and Petrich (2016). Tschaert et al. (2016) observe that one

roadblock to becoming a more data-driven organization is “the human element”. Everyone in the entity

needs to be versed in the tools and techniques of data analytics, and this necessitates a change in attitude

that DA is the domain of the company’s “data analysts” only.

1 https://www.merriam-webster.com/dictionary/mind-set

Copyright Grace F. Johnson 2019. All rights reserved. 4

Bohler, Krishnamoorthy, and Larson (2017) observe that “data analytics are applicable to all

levels of the enterprise, from top level executives to the loading dock crew”, and they insist graduates

entering the workplace have a “data-driven decision-making mindset”. Grant Thornton calls this data-

driven mindset a “culture of analytics” (2016). This creative mindset helps “identify the insights that can

be gained from the data”. According to the panelists at a big data conference held in 2016, “extracting

meaningful knowledge from Big Data requires not only a deep understanding of the data, but also a

creative way of thinking about data” (Huerta and Jensen, 2017).

How can educators and administrators obtain a snapshot of the status of data analytics course

offerings in the undergraduate accounting curriculum? Through a convenience sample of 176 colleges

and universities spanning all 50 states we may start to understand the trend.

Methodology

Institutions from across the nation were selected and requirements for the accounting major

examined. Lists of colleges and universities for each state were obtained from The College Board’s

BigFuture™ website (https://bigfuture.collegeboard.org/). Based on the total number of higher education

institutions in a state, anywhere from one to eight institutions from each state were used for this study.

These institutions are private non-profit and public colleges and universities granting four-year degrees.

Appendix A lists the number of schools chosen from each state.

Further, if a college had two undergraduate accounting programs available – for example, a 120-

hour accounting bachelor’s degree and a 150-hour professional accounting bachelor’s degree – the

unenhanced, traditional four-year bachelor’s degree in accounting was included. Some universities offer

a single 150-hour program granting students both a bachelor’s and master’s degree. These were excluded

from this study.

After choosing the institutions and confirming they offered an undergraduate accounting program

(generally between 120 and 128 semester hours in size), all curricular requirements were accessed and

read. Requirements reviewed are those in effect for the 2017-18 academic year, and include business core

and accounting major requirements and electives.

The Data

A total of 27 institutions (16 percent) either require or identify as an elective for their accounting

majors a course in data analytics. They are listed in Appendix B. Three of these schools include the

analytics course as a requirement in the accounting major; the remaining institutions designate the

analytics course as part of the business core curriculum. Table 1 reports these results and the level at

which the course is offered.

Table 1 – Data for 176 Undergraduate Accounting Programs

Required Elective Neither

Data Analytics Course 22 5 149

100-level 1 0

200-level 7 1

300-level 14 4

400-level 0 0

Copyright Grace F. Johnson 2019. All rights reserved. 5

Although all of the data analytics courses are offered through institutions’ colleges or schools of

business, they vary in course name and the departments providing the courses. Two of the data analytics

courses (both required by the major) are accounting specific, while the other 25 courses have prefixes in

business or related management science disciplines. In Table 2, observe that departments such as finance,

business administration, supply chain, accounting, and management information systems deliver the data

analytics courses.

Table 2 – Illustrations of Course Names and Departments Offering Data Analytics Courses

Course Name Course Number

Business Analytics; and Advanced Data Analysis Lab

(two courses)

SCMA 350 and

SCMA 350L

Financial Data Analysis II FINC 250

Business Information Systems and Analytics GBA 220

Introduction to Business Analytics ISM 3541

Decision-Making Through Visualization and Simulation BUAD 352

Accounting Analytics ACTG 379

Critical Thinking Using Analytics; and Business

Analytics-Tools (two courses)

MIS 3210 and

SCMS 3711

Business Analytics and Financial Modeling ACFM 340

While exposure to data analytics subjects in college is helpful, it represents just one skill set

needed to successfully navigate in a data-based workplace. The next section outlines additional

competencies that will prepare accounting graduates for entry-level positions in the data-driven

workplace.

Essential Data Analytics-Related Skill Sets

There is no shortage of literature suggesting foundational and value-added skills accountants need

in a data-driven world. These har and soft skills are broad and cut across liberal arts, business,

information technology, and communication disciplines. These skills will vary in importance and

frequency of use depending on the accountant’s area of employment (public, managerial, consulting,

government, etc.), position, and career stage. Table 3, derived from references and resources used in this

paper, includes a partial list of skills considered essential or preferable for success in working with data

analytics.

Copyright Grace F. Johnson 2019. All rights reserved. 6

Table 3 – Desirable and Necessary Data Analytics Skills for Accountants

Skills Needed Preferred

Analytical, quantitative, and creative mindset

Applying frameworks and controls to operations and reporting processes

Comfort using advanced technologies such as artificial intelligence (AI)

Communicate findings and make recommendations

Critical thinking

Curiosity and an inquiring nature

Data mining and extraction

Database and information management

Familiarity with the organization and risks facing it

Financial and risks analysis

Forensic IT investigation

Identifying key data trends

Industry knowledge and business acumen

Information systems understanding

Interpreting analytical output

Leadership

Math, computer science, business intelligence

Negotiation

Readily adaptable to change

Relationship building

Strategic problem-solving

Technical accounting knowledge (e.g. budgeting, cost, tax, etc.)

Working in intense environments while maintaining accuracy

Several illustrations from the field describe how these skills and competencies play out in practice.

Research conducted by Robert Half (2016) shows accounting professionals are experiencing a

“talent gap” in the area of data analytics. The top five technical skills in shortage among current

accounting staff are identifying key data trends, data mining and extraction, operational analysis,

technological acumen, and statistical modeling and data analysis. Until accounting curricula catch up and

prepare new graduates with more data analytics-related skills, Goh (2017) recommends that finance and

accounting executives “assemble teams that, as a whole, possess all the requisite skillsets, rather than look

for all those skillsets in a single individual”.

Panelists at a big data conference in 2016 warn about the ongoing stereotype of accountants’

work as scorekeepers and watchdogs, potentially marking accountants for job loss and replacement by

artificial intelligence (AI) systems. However, increased emphasis on data analytics presents a chance for

accounting professionals to shed the old robes of historian and don a new set of clothes suitable for

serving as business partners (Huerta and Jensen, 2017). New entrants to the profession will need

“enhanced analytical skills” and an ability to “think creatively” as a result.

The head of South East Asia for Rothschild stresses that understanding the local business

environment is essential when using data analytics. What ends up being important and a priority at an

organization’s global headquarters might not be considered so at the country level. Because DA

technology permits company data to be examined from an almost unlimited number of dimensions, she

cautions that it can “add to the white noise” and steer staffers into asking “irrelevant questions” (CGMA,

2016). Kogan (2017) warns against charging enthusiastically into data, reminding us that exercising

Copyright Grace F. Johnson 2019. All rights reserved. 7

restraint and “understanding the scope and limitations of the data is imperative, as it enables an

accountant to choose the most appropriate and effective analytical technique”.

A situation relayed by CPA Ireland (2017) tells a cautionary tale. As artificial intelligence,

robotics, and other data analytics-related software offer greater capabilities, the advisory role of CPAs

may decline. Therefore, “accountants or financial specialists who want to maintain a competitive

advantage over other professionals need to get involved in the rise of big data”.

A typical accounting major likely won’t come to an entry-level position fully versed in data

analytics methods and tools. Even a student who has double-majored in mathematics or information

systems and accounting isn’t ready to tackle deep analyses; however, this individual has an advantage.

Following AACSB International curriculum guides, accredited accounting programs will be incorporating

more quantitative and IT courses into their programs. Many undergraduate institutions are not – and do

not plan to seek – accreditation, though, so what are they to do? One CFO of a small publicly traded

company explains: “We look for candidates who have a foundational understanding of analytics and why

it is important to our business to analyze the data and identify trends we can use to proactively manage

the business” (Robert Half, 2016). Coyne, Coyne, and Walker (2017) claim there is less interaction

between information systems developers and accountants, potentially leading to an “increased risk of

capturing unneeded data or failure to capture the right data”. Therefore, they suggest accountants can

close the talent gap by understanding how to create, use, and maintain data, as well as communicate with

information systems staff.

In reviewing the literature about relevant data analytics skills and competencies, “asking the right

questions” or something similar frequently appears. Members of Deloitte & Touche’s Risk and Financial

Advisory group urge business leaders interested in leveraging data analytics to answer financial questions

to clearly frame these inquires. Knowing “what questions the organization is trying to answer enables the

development of more effective and efficient cost and profitability models”. The process of defining

questions can also provide a guide for visual analytics and data analysis overall (Barnes et al., 2018).

A deep knowledge of internal controls (Coyne, Coyne, and Walker, 2017), business processes,

and the “elements of a certain cycle or application” (Kogan, 2017) enhances an accountant’s ability to

choose data for analysis and interpret risks inherent in the results. Similar to this, Richins et al. (2017)

predict that management accountants can open doors to new areas of corporate involvement if they

understand and use data analytics to “develop expertise in strategy formulation and implementation,

monitoring the attainment of strategic objectives, as well as recommending and taking corrective action

where required”.

Tschakert et al. (2016) point out another facet of the accountant’s work impacting the necessary

skill set. They write, “the analytics skills an accountant needs will differ depending on whether a

professional will produce or consume information. Analytics production includes sourcing relevant data

and performing analyses, which is more suitable for junior-level accountants. Analytics consumption is

using the insights gained from analytics in decision-making and is more relevant for senior-level roles.”

Cokins (2015) presents a vision of what data analytics is capable of doing. He explains that DA

tools have been used for a while to answer the question “what?”. Now, savvy businesspeople are turning

to DA to answer two more questions: “so what?” and “then what?”. Finance and accounting

professionals can alter how others perceive them by becoming instrumental in the process to “link more

operational and financial data, make information available sooner and provide a richer set of data” for

decision makers.

Copyright Grace F. Johnson 2019. All rights reserved. 8

How has the work of accounting departments been altered as a result of data analytics? At the

facility of a global heavy industrial manufacturer, its treasurer observes her “accounting department is

much more efficient and proactive in management decisions with the increased help of data analytics. By

saving time completing tasks, it allows more human brain time for process improvement decisions for the

business” (Nelson, 2018). Excel is the tool her staff uses analytics tasks. The director of internal audit of

a nationwide fast food corporation notes that all aspects of internal auditing are touched by data analytics.

She explains that DA are incorporated into every stage of financial, operational, and compliance audits.

“We use it in planning to know where to look for deeper analysis, like the sales compensation audit. We

ran it during planning and identified some outliers where there were an abnormal number of salespeople

on smaller contracts or fewer salespeople on larger contracts. During the actual testing and execution

phase we use it to run for compliance testing – SOX for example and for testing any big swings in

balances outside of normal, testing all journal entry transactions to see if any did not have proper

approval, etc. And then we also use at the end of an audit for continuous monitoring to watch real time if

something is an anomaly” (Huff, 2018a). She says analytics tools employed in her workplace depend on

the type of tasks, but ACL, Tableau, and Excel are the most commonly used applications.

Appendix C presents comments from several faculty whose accounting majors are required to

take a data analytics course.

Recommendations for Accounting Faculty What can faculty do to acquaint undergraduate accounting majors with the professional, business,

and technical data analytics skills sought by prospective employers? This final section of the paper offers

suggestions to undergraduate program accounting faculty about ways they can introduce and foster entry-

level DA skills and competencies. Coming from practitioner articles, white papers, and personal

inquiries, these recommendations are clustered into three categories: professional; business; and technical.

But as employers’ use of data analytics varies, faculty must choose the level of theory, concepts, and

hands-on work to incorporate into their courses. Factoring into this choice are faculty knowledge of and

experience with data analytics, cost to acquire and support DA software tools, and whether the subject

should be taught as a stand-alone course or blended into existing accounting courses.

Professional

PwC (2015) looks for leadership potential, conducting research, professional skepticism, and a

dedication to lifelong learning (liberal arts institutions in the United States have promoted this for several

hundred years). Being a “student of how the world works” is mentioned as an important new-hire quality

by PwC (2015). The treasurer of the manufacturing company quoted at the top of this page stresses her

corporation looks for “people who are willing to change and adapt as process improvements are

implemented – a team player not afraid to share knowledge, think openly, move forward, and make

decisions” (Nelson, 2018). “Critical thinking and analytical abilities to help with thinking through what

analytics to run and then also how to read the results and what the data is telling you” (Huff, 2018b) are

competencies that show business awareness. An accounting alumni focus group conducted as part of one

college’s accounting program review ranked written communication and oral presentation abilities as the

most desired skills for entry-level accountants (Johnson, 2018).

In their comment letter to International Federation of Accountants (IFAC) about the use of data

analytics in auditing, the chair and CEO/president of NASBA (National Association of State Boards of

Accountancy) confirm the IFAC opinion that “data analytics should not be seen as a replacement for the

auditors’ professional judgment and skepticism in performing their work. An auditor’s intuition is not

something that can be replaced by use of data analytics or other forms of technology” (NASBA, 2017).

Therefore, while the capability of data analytics and AI in business and accounting continues to expand,

Copyright Grace F. Johnson 2019. All rights reserved. 9

principles such due care and those outlined in professional codes of accounting conduct are essential

elements to teach in accounting classrooms.

According to Christian Rast, Head of Global Data & Analytics at KPMG, “trust is an integral

component of successful, mutually beneficial relationships between people, societies and global

economies. It’s instinctive. We rely on trust to approach the uncertain or the unknown with confidence.

However, during times of rapid disruption and growing complexity, our instinctive ways of judging

trustworthiness can be tested and this has consequences” (Erwin, 2017). How does this relate to data

analytics? One data analyst from a federal government agency says respect for and trust in data analytics

is an important dimension of organizational culture (Couch, 2018). Students need to understand what is

valued by the workplace culture; this is not an easy skill for recent college graduates to develop, however.

For instance, if a workplace culture places significance on data-driven decision making, the sooner new

employees sense this culture the better off they might be in quickly adapt to workplace expectations.

Business

Preparation to enter a business environment characterized by greater reliance on data isn’t solely

focused on acquiring and developing technical mathematical and computer skills. PwC released a white

paper in 2015 that recommends changes to the accounting curriculum for students entering a “data

driven” workplace. In addition to specifying several courses to be added to the undergraduate accounting

curriculum and the content of those courses, PwC places importance on students’ having a solid

understanding of business. The white paper suggests integrating practical and actual situations permitting

student to have “a grasp of processes and how data flows through an organization, so that they know the

right questions to ask clients. This can be taught in a class dedicated to scenarios in which students look at

mocked-up data to determine what is wrong and why it is so (technical accounting skills); how they would

test it (auditing skills); and how they would communicate it to others (communication and business

writing skills)” (PwC, 2015).

Other valuable business skills for the entry-level accounting professional include an

understanding of emerging economies, business strategy, organizational models, corporate governance,

and regulatory agencies (PwC, 2015). Keeping up with changes in accounting authoritative literature

through reading and research emerged as an important skill mentioned by the accounting alumni focus

group participants (Johnson, 2018).

Technical

Abilities to work with Excel at an intermediate or advanced level were mentioned by several

interviewees for this project. The internal audit director noted “technical skills would include knowing

how to write scripts using tools like ACL or others” (Huff, 2018b). PwC (2015) suggests adding courses

in programming and advanced statistics.

For institutions whose accounting programs are separately accredited, the AACSB International

stresses the importance of a “dual focus” in the accounting curriculum for “data analytics or business

analytics along with appropriate IT skills and knowledge development” (AACSB International, 2014). It

notes that going beyond the usual statistics and information technology content of a traditional business

core is an expectation of the newest information technology education standard for AACSB accredited

schools.

The panel members of the 2016 big data conference (first noted on Page 4 of this paper) hint at

the need to expand coverage of data analytics topics into multiple accounting courses. They express

concern that “most accounting students take a single AIS course, which, in the panel’s opinion, is not

sufficient to address the analytic skills students need if analytic skills are only taught in AIS courses”

(Huerta and Jensen, 2017).

Copyright Grace F. Johnson 2019. All rights reserved. 10

In its Uniform CPA Examination Blueprints effective for July 1, 2018, the AICPA lists

recognition of “the role of big data/data analytics and statistics in supporting business decisions” as one of

the areas for inclusion on the Business and Economic Concepts section of the examination (AICPA,

2017). Although it is a minor element and planned for testing at the remembering and understanding skill

level, accounting faculty might find this addition to the exam another motivator for including at least

some coverage of data analytics in their courses.

Concluding Comments and Call to Action Is the attention garnered by data analytics short-lived or will undergraduate program faculty make

significant efforts to deliver appropriate content – even requiring data analytics courses – for accounting

majors? Faculty should not quickly jump on the data analytics bandwagon without a careful

consideration of the following aspects of their accounting programs.

Number of accounting faculty and their experience with data analytics

Job placement of accounting program students

Institutional curricular constraints

Department philosophy and culture

The number of accounting faculty in a department as well as their experience with data analytics

certainly will guide how the subject can be incorporated into the accounting program. In a small college

with one or two fulltime accounting professors this may feel like “one more thing” to be wedged into a

course in managerial accounting or accounting information systems (if the school even offers an AIS

course). On the other end of the spectrum, a school of accountancy or a very large department of

accounting may have the staffing flexibility to ask for volunteers (or mandate) the teaching of data

analytics in one or more accounting courses. Perhaps another department housed in the college of

business can open its data analytics course to accounting majors. For faculty interested in and willing to

take on teaching the subject, there are questions about where to obtain training and who will pay for it.

Deciding how to incorporate data analytics into accounting courses also depends on the

employers and jobs into which new accounting graduates enter. Its heightened importance for public

accounting firms, particularly larger ones, makes DA a critical competence for success in securing a

position. Thus, if the majority of students in a program choose public accounting as their career starting

point it seems necessary for them to have more than a general familiarity with data analytics. Are your

students heading to work in state or municipal government accounting or finance offices? Perhaps less

hands-on experience with DA and more understanding of what it is and how it can be used is the

appropriate level of coverage of the subject. Faculty can contact a group of current and potential

employing organizations to determine the extent of DA knowledge these employers desire, then design

appropriate content into their accounting courses.

A third aspect with the power to influence this decision – and perhaps the most limiting of the

four – is constraints imposed by institutional curricular designs. Does the curriculum restrict the size of a

major, require a minimum number of credit hours to be taken outside of the department or school, or

contain other provisions that challenge faculty to add new courses (in this case, a new course in data

analytics)? Perhaps it is time to draw on the collegiality and social capital of a faculty member and

approach the school or department where data analytics is taught. Is that course available to all students,

or only those in an analytics-related major? Would the data analytics professor be willing to open the

course to accounting students as an elective? The direction these conversations could take are affected by

the institution’s political atmosphere, but also by the imagination and creativity of those having the tête-à-

tête.

Copyright Grace F. Johnson 2019. All rights reserved. 11

Finally, the accounting department’s or school’s philosophical and cultural environment shapes

the discussion about data analytics and the accounting curriculum. Traditional and conservative, risk-

taking and experimental, realistic and cautious, or leading-edge and insightful. Which word-pair best

captures the spirit of your accounting colleagues and you? Making the data analytics-in-the-curriculum

discussion even more challenging is when there are seemingly competing philosophies and cultures in the

accounting department. Working through these differences takes time and demands a lot of patience and

open-mindedness. One useful approach is to attempt to put aside personal preferences and to think of the

students: what choices would help prepare them to meet the varied demands of their internship and first

fulltime jobs after graduation?

How will you filter out the distractions and answer this question? If you are not already

including the data analytics topic – or an entire course on the subject – in your accounting curriculum,

what is the plan for your accounting colleagues and you to study its need? Maybe the first steps are

informal discussions or independent reading on the subject. Once each of you is better informed the

group is positioned to craft a roadmap for action. But one thing is certain: whether added into the

undergraduate accounting curriculum or not, accounting faculty cannot ignore the topic of data analytics.

Copyright Grace F. Johnson 2019. All rights reserved. 12

Appendix A – Number of Colleges and Universities Sampled from Each State

State

# of

Schools

State

# of

Schools

Alabama 4 Montana 2

Alaska 1 Nebraska 2

Arizona 4 Nevada 1

Arkansas 3 New Hampshire 2

California 8 New Jersey 3

Colorado 4 New Mexico 2

Connecticut 2 New York 8

Delaware 2 North Carolina 5

Florida 6 North Dakota 2

Georgia 4 Ohio 7

Hawai’i 2 Oklahoma 3

Idaho 2 Oregon 3

Illinois 6 Pennsylvania 8

Indiana 4 Rhode Island 2

Iowa 4 South Carolina 4

Kansas 3 South Dakota 2

Kentucky 3 Tennessee 5

Louisiana 3 Texas 7

Maine 2 Utah 2

Maryland 3 Vermont 2

Massachusetts 4 Virginia 4

Michigan 4 Washington 3

Minnesota 5 West Virginia 3

Mississippi 2 Wisconsin 4

Missouri 4 Wyoming 1

Copyright Grace F. Johnson 2019. All rights reserved. 13

Appendix B – Institutions Where a Data Analytics Course is Required or an Elective

Name

Required or

Elective

Auburn University R

Austin Peay State University R

Baylor University R

Bucknell University R

Carthage College E

Christopher Newport University R

City University of New York: York College R

College of William & Mary R

Florida Gulf Coast University R

Florida State University R

Michigan State University R

Middle Tennessee State University R

Montclair State University R

Oregon State University R

Pennsylvania State University – State College E

Rutgers University E

Truman State University R

University of Akron R

University of Central Oklahoma R

University of Cincinnati R

University of Memphis R

University of Nebraska - Lincoln R

University of Rochester R

University of South Carolina E

Utah State University R

Wright State University R

Yeshiva University E

Copyright Grace F. Johnson 2019. All rights reserved. 14

Appendix C – Comments from Faculty at Institutions Requiring a Data Analytics Course for

Accounting Majors

The University of Rochester’s Simon School of Business requires a

data analytics course for students in its accounting program, a track within the

undergraduate business major. Professor Heidi Tribunella of the Simon School

explains, “The Simon School of Business has had an analytical approach to

business, historically, and we are currently highlighting that approach given the

industry push in analytics in accounting, marketing and business

overall. Many recruiters when they visit campus are looking for students with

analytical skills. It is our belief that no matter what area of business you are

in, you should always approach a business decision with data and to make the

best decision you need to analyze that data. Therefore, we made the decision

to include analytics in the core of our business program” (Tribunella, 2018).

Dr. Thomas Calderon details why the University of Akron included

analytics in the business core. “The College of Business Administration (CBA)

attempts to provide employers with students that are prepared with cutting edge

knowledge and skills. As such, we regularly engage our large numbers of

executive advisory board members to provide feedback on our college core

curriculum as well as major specific topics. One topic that kept coming up

from advisory board members, as well as from other employers, was the

importance of analytical thinking. During a series of faculty retreats, the

feedback from external stakeholders was assessed and the college core

curriculum was revised to change courses called Quantitative Business 1 and

Quantitative Business 2 to be more reflective of modern business needs. This

resulted in 3-course series called: 1) spreadsheet modeling, 2) business

statistics, and 3) business analytics. These courses provide the basis for more

in-depth quantitative courses within the separate majors. The business

analytics course teaches students how to descriptively approach big data (how

to clean, visualize data, and summarily represent data) in addition to providing

advanced tools for data analysis and prediction (e.g., principal components,

multiple and logistical regression, decision trees, clusters, neural networks,

etc.). Thus, currently all majors within the College of Business

Administration, including accounting, are exposed to the basic theories and

skills associated with analytics. The CBA core includes a sub-core of three

analytics courses, comprised of (1) Spreadsheet Modeling, (2) Business

Statistics, and (3) Business Analytics. Accounting and all other CBA students

must complete the analytics sub-core to earn a business degree” (Calderon,

2018).

At Carthage College, Dr. Catherine Lau, chair of the Accounting and

Finance Department, explains why data analytics is an elective for the

accounting major. “We feel that it is important for any field that students be

able to analyze data and then draw and test conclusions. Since we are a liberal

arts college, we do limit how deep any one major can be, so give students a

number of electives to choose from. Students who will sit for the CPA are

more likely to take any additional tax or government accounting course than

data analytics, but those going into corporate finance may elect the data

analytics” (Lau, 2018).

Copyright Grace F. Johnson 2019. All rights reserved. 15

Auburn University’s School of Accountancy director, Dr. Jennifer

Mueller-Phillips, writes “Auburn University requires all business majors to

take a two-course business analytics sequence. We then encourage accounting

students to pair a minor in business analytics with their accounting major,

adding several more analytics courses to the sequence. Within the accounting

major, students also take an accounting analytics course focused on data

analysis and visualization of accounting data. Our employers are emphasizing

that graduates have these skills. We have a responsibility to prepare them for

the profession” (Mueller-Phillips, 2018).

Copyright Grace F. Johnson 2019. All rights reserved. 16

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