The next frontier in data analytics

Why CPAs and organizations need to learn to use advanced technology to predict and achieve outcomes.
By Norbert Tschakert, CPA/CFF/CITP, Ph.D.; Julia Kokina, CPA, Ph.D.; Stephen Kozlowski, CPA, CGMA, Ph.D.; and Miklos Vasarhelyi, Ph.D.

The next frontier in data analytics
Image by DavidGoh/iStock

As technology continues to evolve, it promotes changes to business models and surprises those who are unprepared. Businesses change their strategies and the way they operate. New threats and opportunities arise. In an increasingly data-driven world, CPAs need to be able to adapt to these technological disruptions.

CPAs now often find themselves performing tasks that require skills in data analytics:

  • Both internal and external auditors are using data analytics to enable practices such as continuous monitoring, continuous auditing, and analysis of full data sets in situations where only samples were audited.
  • Financial planning and analysis professionals analyze data in hopes of discovering the best course of action for their companies.
  • CFOs and finance leaders use Big Data to find patterns in customer behavior and market trends to drive company strategy.

In an effort to advance the use of analytics in auditing, the AICPA and Rutgers Business School in December 2015 announced a research initiative ( focusing on integrating analytics into the audit process and on defining how analytics can be used to enhance audit quality. Potential improvements include producing higher-quality audit evidence, reducing repetitive tasks, and better correlating audit tasks to risks and assertions. The AICPA will use the findings from this initiative to inform guidance on audit data analytics for CPA firms of all sizes.

A joint AICPA Assurance Services Executive Committee/Auditing Standards Board Task Force is developing a new Audit Data Analytics Guide, which will supersede the current Analytical Procedures guide. This new guide will carry forward much of the content included within the Analytical Procedures guide but will also include guidance on using audit data analytics throughout the audit process. Related projects are also underway to create voluntary audit data standards, which help with the extraction of data and facilitate the use of audit data analytics, and a tool to help illustrate where audit data analytics can be used in a typical audit program.

Meanwhile, mastery of data analytics can help businesses generate a higher profit margin and gain a meaningful competitive advantage. Some experts even predict that companies ignoring data analytics may be forced out of business in the long run. As data analytics is an area where change may occur more quickly than companies or CPAs may adapt, change management concepts should be considered to take advantage of the opportunities data analytics can bring.

Given that the price of computer hardware and cloud services has been ever-decreasing, what exactly stands in the way of companies being more data-driven? It is the human element.

"The human element of data analytics is the most critical factor in building a successful program," said Roshan Ramlukan, EY principal and global assurance analytics leader. "But it's also the least understood and an impediment to further growth in this area. Leaders must also recognize that analytical skills must be developed in all of their people, not just a few data analysts." (See the chart "Importance of Analytics Skills" for CFOs' ranking of the importance of data analytics skills for accounting and finance staff.)

Importance of analytics skills

Expertise in business analytics, such as business intelligence and data mining, was deemed mandatory for at least some accounting and finance employees by 61% of more than 2,100 CFOs participating in a 2014 survey by staffing resources firm Robert Half.

Q: How important are business analytics skills, such as business intelligence, for your accounting and finance employees?

Importance of analytics skills

Source: Robert Half survey.

To better explain skill development in data analytics for CPAs, we first divide data analytics into four types as shown in the chart "4 Types of Data Analytics."

4 types of data analytics

Data analytics is often misunderstood as descriptive analysis ("what is") only. The real value, however, lies in predictive ("what will be") and prescriptive analysis ("What should we do?"). Data analytics is highly relevant as companies and industries transform to take advantage of technological innovations, and as expectations of regulators and investors with regard to data availability and analysis are increasing.

Many accountants already use descriptive analytics in their daily work. They compute sums, averages, and percent changes to report sales results, customer credit risk, cost per customer, and availability of inventory. Accountants also are generally familiar with diagnostic analytics because they perform variance analyses and use analytic dashboards to explain historical results. This, however, is not sufficient.

"I work with many organizations, and I see that, unfortunately, the accounting function is probably one of the least analytical functions in large organizations," said Tom Davenport, Ph.D., a professor of information technology and management at Babson College in Babson Park, Mass. "Clearly, it has a very strong transactional orientation, at best doing descriptive analytics focused only on structured small data. The various attempts to try to predict financial performance, getting into nonfinancial performance measures that might be good predictors of financial performance, have never really gone anywhere despite a lot of discussion over the years. This presents a great opportunity for accountants to provide a much more valuable role to management. Accounting not only has to catch up with analytics, but also needs to keep up with this rapidly changing field on an ongoing basis."

Predictive analytics and prescriptive analytics are now required because they provide actionable insights for companies. Accountants need to increase their competence in these areas to provide value to their organizations. Predictive analytics integrates data from various sources (such as enterprise resource planning, point-of-sale, and customer relationship management systems) to predict future outcomes based on statistical relationships found in historical data using regression-based modeling. One of the most common applications of predictive analytics is the computation of a credit score to indicate the likelihood of timely future credit payments. Prescriptive analytics uses a combination of sophisticated optimization techniques (self-optimizing algorithms) to suggest the most favorable courses of action to be taken.

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. Similar to a driver who doesn't know exactly how all the car's parts are working, CPAs do not need to become data scientists or computer engineers to benefit from the coming data revolution. It is most important that CPAs become more proficient consumers of analytics to both enhance their current audit practice with available technology as well as support their client base in undertaking data analytics activities. (See the chart "How Skills Are Acquired" for the methods organizations use to improve their employees' data analytics skills.)

How skills are acquired

In-house training is the most common method companies are using to improve employees' business analytics skills, according to a 2014 survey of more than 2,100 CFOs by staffing services firm Robert Half.

How skills are acquired


Producing analytics starts with understanding the business objective ("What are the key questions that you expect the analysis to answer?") and identifying and obtaining relevant internal and external data sources to support the analysis. Ramlukan said producing analytics often occurs at the junior level. He explained that the ideal "analytically skilled" employee has these three characteristics:

  • Good technical skills: Understands the data and knows how to manipulate it.
  • Understanding of the business context: Can distill a business problem or opportunity into key questions to be answered and understands the business data flow and the relationship between objects within the business context.
  • Analytical mindset: Possesses an inquiring nature and intellectual curiosity.

As this ideal employee is a rare find, companies adapt by building teams of various specialties and technical skills. Production users need to have superior technical skills while consumption users should have a significant understanding of the business context. Both production and consumption users require an analytical mindset.

"We differentiate candidates who are experienced in data exploration, data visualization, and predictive modeling," said Brad Ames, CPA, internal audit director at Hewlett-Packard.

These skill sets are not common among accounting firm personnel, Ames said, so when HP recruits for these positions, it posts job titles such as "data scientist" or "analytics solution architect."

At the same time, accountants may lack the know-how about educational resources and best practices. A great way to get started on applying data analytics to the audit function is to improve one's knowledge of basic building blocks such as Excel and Access, and audit analytics tools such as ACL and IDEA.

At larger accounting firms, analytics is used regularly in tax, auditing, consulting, and risk management. Ramlukan said data analytics is a skill that can be applied to many scenarios across all service lines. Employees who have this skill are therefore both very versatile and valuable to the organization. The work of CPAs will advance in the future to provide more data analysis, consulting, and decision-making support services. The audit function in particular will undergo a significant change with the incorporation of data analytics techniques. Data analytics can thus provide an important business opportunity for CPAs at accounting firms. CPAs at other organizations also are developing data analytics capabilities to support their needs.

"Over the years, I've leveraged data analytics tools for a variety of internal audit projects and continuous monitoring activities," said Joel White, CPA, CGMA, AICPA director—Internal Audit, Risk & Compliance. "The tools' intuitive interface allows the user to quickly gain a grasp of large quantities of data and test 100% of various data attributes, as opposed to sampling a small percentage. Several examples of where I've used data analytics tools to analyze large data sets include corporate card transactions, payroll processing, vendor payments, health care charge capture, and customer/vendor deduping."

Data analytics tools have also been incorporated into continuous auditing/monitoring activities. HP uses data analytics technology to extract relevant transactional data and files from the database that holds accounting data and also extracts and performs analytics over unstructured data sourced externally. This information is provided to internal audit staff, external audit personnel, and business unit management to support oversight or operational activities.

Given that most traditional degree-granting accounting programs do not require courses in statistics and data analysis, CPAs and accountants should seek additional training in these areas. (See the chart "Learning Opportunities Ranked" for a comparison of the educational value of different skill development methods.)

Learning opportunities ranked

When deciding on the most suitable path to data analytics education, we suggest considering the following options, which are ranked according to how employees could best approach data analytics, beginning with developing an initial understanding, developing an analytical mindset, and then acquiring specific data analytics skills. This graphic introduces these learning opportunities and ranks them by their potential for skill development.

Ranking of 10 learning opportunities by potential for skill development

Learning opportunities ranked

In light of the changing nature of accounting practice, companies look for talent with a new set of skills. Ames said, "The skill to deploy assurance technologies and utilize a variety of financial and nonfinancial data is highly valued."

A 2015 PwC report, Data Driven: What Students Need to Succeed in a Rapidly Changing Business World, cites the following examples for technical data analytics-related skills that accounting professionals should have or obtain:

  • Research and identify anomalies and risk factors in data. Consider new sources of data.
  • Understand relational and nonrelational databases.
  • Use simple vendor risk dashboards and filters to minimize inefficiencies and human error.
  • Perform data and process mapping from a regulatory and risk-assurance view.
  • Use exploratory multivariate statistics, inferential statistics, visualization tools, optimization methods, machine learning, and predictive analysis tools.
  • Identify and frame key business decisions and their related metrics to make these solutions more effective and efficient.


The advent of data analytics offers both challenges and opportunities for CPAs. The challenges include undertaking appropriate training to develop the skills needed to initiate and support data analytics activities, as well as altering the present audit model to include appropriate audit analytics techniques. The opportunities include a technology-rich audit model that provides for greater thoroughness, efficiency, and accuracy, as well as new business opportunities to provide data analytics expertise to CPAs' clients and organizations. CPAs, whether working in public practice or industry, will enhance their career opportunities through the acquisition of additional data analytics expertise. 

About the authors

Norbert Tschakert ( is an associate professor of accounting at Salem State University in Salem, Mass. Julia Kokina ( is an assistant professor of accounting at Babson College in Babson Park, Mass. Stephen Kozlowski ( recently graduated from the Ph.D. program in accounting at Rutgers University and will be an assistant professor of accounting at Eastern Illinois University in Charleston, Ill. Miklos Vasarhelyi ( is a professor in the department of accounting and information systems at Rutgers Business School in Newark, N.J., and is a leader of the Rutgers AICPA Data Analytics Research Initiative.

To comment on this article or to suggest an idea for another article, contact Ken Tysiac, editorial director, at or 919-402-2112.

AICPA resources


CPE self-study

  • Analytics and Big Data for Accountants (#746271, text; #164211, one-year online access)
  • Big Data (#165211, one-year online access)

For more information or to make a purchase, go to or call the Institute at 888-777-7077.


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