Data analytics for accounting. Why | What | How

 Accountants are responsible for compiling and analyzing financial data to ensure a business operates efficiently. However, with data analytics, accountants can use this information to improve overall business processes. Data analytics can help identify inefficiencies, track spending and revenue, and optimize operations. By incorporating data analytics into their workflow, accountants can provide critical insights that help businesses grow and succeed.

With the recent changes brought about by Big Data and data analytics, a well-prepared accounting job candidate must have expert-level accounting and data analytics skills. 

Why data analytics for accounting is important?

There is no doubt that data analytics is one of the most disruptive issues facing the accounting profession today. Almost daily, accounting firms and global organizations cite this transformation as a must-have skill set for accountants. Unless accountants embrace and adapt to the new world of Big Data, data science, and, by extension, artificial intelligence and automation, they may become the "weavers of the 21st century". 

Google search results for "data analytics and accounting" reveal page after page of articles that alternate between dire warnings and exciting predictions of what the accounting profession can expect.

Data analytic techniques have the potential to replace many of the tasks traditionally performed by accountants and auditors. Accounting and auditing tasks at the entry-level, such as posting and collecting receivables, have already been automated. Shortly, complex tasks that accountants currently perform, such as business analysis, external reporting, and auditing, may also become automated since these tasks are routine and do not require machine-inimitable skills.

What is data analytics?

Data analytics aims to gain insight and knowledge by analyzing data trends.

Big Data is typically described by the 

• V's of Big Data (volume, velocity, and variety) and 
• the V's of organizational data (veracity, visualization, viscosity, and virality).

 However, Big Data has little value—the value of Big Data is only realized when an accountant effectively leverages data with a mix of critical thinking and technology. In other words, Big Data, combined with data analytics, can answer many important questions. Thus, the real value of Big Data-data analytics is not in the size and complexity of the data, but rather in the quality of how the Big Data is used to create actionable intelligence (achieving the overall V of value)–—which involves using the correct data with the right analytical tool.

Why is data analytics important in accounting?

Accountants possess a unique skill set to improve an organization's data analytics. In other words, "Your Data Won't Speak Unless You Ask It the Right Data Analysis Questions."

Given accountants' knowledge of business processes and how information flows through the organization, accountants can 

• Help management creates specific questions that get to the heart of the analysis.
•  Accountants thoroughly know an organization's data to determine what internal data answers the question and what additional (external) information should be gathered. 
• Accountants also possess unique accounting domain knowledge that helps interpret and communicate data analytics results. 
• Specifically, accountants ensure that the answers to the questions make sense within the organization's business model; can be implemented, and are aligned with the forecasts, budgets, and key performance indicators. 
• As accountants have prepared internal and external reports for multiple constituents, they also understand the best modes of presentation for the intended audience.

What impact is data analytics having on accounting?

Example 1

One example of the convergence of Big Data and data analytics and the "sweet spot" is warranty expense. Using historical warranty expense data, 

• Descriptive and Diagnostic Analytics could show the average claim and trend and the products with pending claims. 
• Predictive analytics could predict a trend line. 
• Externally available, unstructured data (i.e., Big Data) could include websites with customer comments and complaints. 
• Sentiment analysis (i.e., Prescriptive Analytics) could examine these external data, classify comments as positive and negative, and determine the underlying reasons for the claims. It could also capture a change in the tone, predict customer behaviour, and estimate future demand. 
• Adaptive and Autonomous Analytics could do all those things and adjust the model as the conditions warrant. 

Thus, combining the data analytics results on the internal and external data would allow a company to predict significant changes in warranty expense and respond quickly to any problems, minimizing cost and reputational impacts.

Example 2

For example, Allowance for Doubtful Accounts, estimated by the aging of receivables, assumes only one thing—that they are late. But with Customer Relationship Management systems and other data, accountants could:

• determine whether the customer has a dispute with the items received (causing the non-payment).  
• Determine whether the customer is continuing to purchase from the company.
•  Develop statistical models that predict allowances and collections based on all available data. 

How are accounting bodies changing their curriculum?

Various accounting licensing associations across the globe are adding Big Data and data analytics concepts to certification exams (e.g., the American Institute of CPAs, the U.K. Association of Chartered Accountants, the Association of International Professional Accountants, and the Institute of Management Accountants). Some of the noted curriculum changes are:

Association of Chartered Certified Accountants (ACCA) - The use of data analytics tools. Using Big Data and data analytics to improve accountancy and audit effectiveness is incorporated in various subjects.

Certified Public Accountant (CPA) (U.S.) - Use and interpret data analytics output. From 2024, Data analytics is one of the papers broadly covered. Understanding business systems, controls, and risk; Data management and analysis.

Chartered Institute of Management Accountants (CIMA) - Use Big Data analytics to inform organizational decisions. The topic is examined in both the management level and strategic level exams.

CPA Australia - Use analytical tools and models to analyze the industry and the organization's market. Use analytical tools and models to understand and measure the organization's performance.

Institute of Management Accountants (IMA) - Topics include business intelligence, data mining, analytic tools, and data visualization.

The CMA exam has been completely transformed. Part 1, which used to be called Financial Reporting, Planning, Performance, and Control, has been retooled to Financial Planning, Performance, and Analytics. The CMA exam eliminated internal auditing favouring technology, analytics, and integrated reporting. 

Technology and analytics now account for 15 per cent of Part 1's content (up from 0 per cent), which was made possible by reducing the weight on other topics. Topics in this new section include business intelligence, data mining, analytic tools, and data visualization. A complete overhaul of the CPA and CMA exams indicates that accountants' skill sets have changed, with data analytics becoming more important.

How can accountants use data analytics to improve business processes

Data analytics is "the heart of every business". Therefore, accounting professionals need to develop an analytics mindset to remain relevant. EYARC (2017) states that an analytics mindset is the ability to

1. ask the right questions,
2. extract, transform, and load relevant data,
3. apply appropriate data analytic techniques, and
4. interpret and share the results with stakeholders.

While the last step specifically mentions interpretation,

•  Accountants can play several interpretation roles throughout the analytics mindset. Specifically, accountants can serve a critical role as an interpreter between management and data scientists/analysts/ administrators, helping these technicians understand what information the decision-makers (i.e., management) need/want.
• Accountants can help guide the analysis by determining which questions might be addressed by data, identifying relevant data to answer the questions, using appropriate analytic techniques, and interpreting the results by giving them context and meaning within the organization. 
• Thus, not only do accountants help prepare the accounting data, but also they help perform data analytics to allow decision-makers to make informed decisions. If accountants choose not to fill this role, audit firms and companies will hire data analysts and scientists and then teach them accounting.

Which data analytics skills does an accountant need?

EY in their digital mindset editions (EYARC2017) and PWC both have expressed that the accountant will have the following essential skills for the future.

• Ask the right questions and formulate hypotheses.
• Construct experiments, and gather and analyze data needed to make evidence-based decisions.
•  Extract, transform, and load relevant data (i.e., the ETL process).
• Apply appropriate data analytics techniques.
• Possess strong quantitative skills in statistical analysis, visual analytics, machine learning, and the ability to analyze unstructured data.
• Code and understand Big Data technology structures.
• Interpret and share the results with stakeholders.
• Possess strong (oral) communication skills.

How do I learn Data analytics for accounting?

Suppose you're looking to stay ahead of the curve in accounting. In that case, it's important to ensure you have the skills to work with data analytics. The AICPA offers a variety of data analytics certifications that will help you do just that. With these credentials, you'll be able to provide critical insights and analysis to help businesses grow and succeed. So if you're looking to take your accounting career to the next level, consider earning an AICPA data analytics certification.