Certain buzzwords, such as blockchain, artificial intelligence, machine learning, and data analysis, have become increasingly common in the accounting profession as technology plays a bigger role in the day to day aspects of a business. While all of these buzzwords and their related concepts are important, none seems to be quite as important as data analytics.

Data analysts and data scientists are becoming commonplace in most businesses as companies try to sift through the huge amounts of information they receive on a daily basis. This isn’t without cause; digging into and interpreting this data effectively can significantly improve business intelligence and decision making.

As a future accountant or CPA, you need to be aware of big data analytics and its impact on the accounting profession, and every industry in general. To help you get started, let’s take a closer look at how data analytics may affect your future in the accounting field.

What is data analytics?

Simply put, data analytics is analyzing data. It’s the science of taking raw data and formulating it in a way that’s understandable, allowing conclusions to be drawn from the information. The mass of information that can be collected via technology is overwhelming; without data analytics and data science, this information would mean nothing. Data analytics – whether through automated or human-driven processes – helps takes this mass of information, process it for human consumption, and provide meaning within the information.

Ideally, data analytics would be used to formulate conclusions and help a company recognize trends and metrics that otherwise would be lost in the mass of information. Companies can then use this information to improve in a variety of areas. Data analytics can be used both inside and outside of the organization; companies use it to improve internal processes, analyze market trends and customer wants, and improve products and services.

To get a better idea of big data analytics, let’s consider CPA Exam scores and how candidates arrive at those scores. There are four sections of the CPA Exam, and thousands of candidates take each section every year, and each incident has its own unique score associated with specific candidates and a specific exam. At a high level, we have thousands of lines of unstructured data from various data sources that don’t really mean anything. However, if we take this data and break it down into average scores per testing window, we get meaningful data that can help us make decisions about the CPA Exam.

We can even add more factors to increase the amount of data we collect, such as average study time or review course used. This exponentially increases our data points, but once we use data mining and analyze the data to create meaningful information, we can easily see the average study time per candidate per exam, or which review course seems to help the most candidates pass the exam.

What is data visualization?

Once raw data has been converted into a readable and usable format via data analytics, conclusions can be drawn and action items can be created. However, looking at the data simply through a text-based lens isn’t always the best way to recognize trends.

Data visualization represents data through charts, graphs, or other visual aids, allowing patterns in the data to be easily identified. Good data visualization utilizes the best type of visual aid for the data, as well as colors and patterns that keep the audience interested. As big data continues to become a larger part of the business landscape, and millions of rows of data are being generated on a daily basis, communicating the most useful information is becoming increasingly important.

If we consider our CPA Exam score example from above, we can visualize it two ways. The first way is a dataset, or listing of data points. For example, let’s say we have a listing of condensed data showing the average score for each exam in each testing window for the last five years. The listing will definitely give us information, but it may not help us easily see which quarters have the highest scores on each exam. If we put this information on a line graph, we may see that there are higher pass rates in the Q2 and Q3 testing windows than the Q1 and Q4 testing windows. Not only will the information be more readily available, but it will be more visually appealing, helping us to take meaningful information away from what was initially just a listing of data.

How accountants use data analytics

In the current business climate, all industries are driven by big data including accounting. Accountants within both public accounting and industry need to know how to work with data to make strategic business decisions and meet client demands. Below are several ways clients use big data analytics within an organization.

Monitor and improve business performance

Accountants work in every industry, from healthcare to entertainment to non-profits. Each of these industries needs to be constantly evaluating business performance in order to stay healthy and profitable. Accountants can use data analysis when reviewing financial information to ensure the company is running well, meeting goals, and maintaining or improving performance. This knowledge is essential to both a business’s sustainability and survival.

Identify and manage risk

CPAs, CMAs, and anyone working in the financial or accounting side of a business need to know how to work with risk. Risk can come from a variety of areas within and outside of the organization. Client retention, the protection of business assets, and internal controls are just a few examples of areas where risk can be identified and managed. By having understandable data points to work with, accountants can review the various areas of risk facing a company and use predictive analytics to make business decisions around specific risks, leaving the company with a more clear future.

Improve the client experience

Accountants working in public, industry, or a small business capacity all have clients. Data analytics can be used to improve the client experience by looking at factors such as the time it takes to complete an audit, the turnover of tax returns, or general client satisfaction surveys. This helps accountants and firms to retain and bring on new clients.

How to become more literate in data analytics

One of the most important things a CPA candidate, current or future accountant can do to become more literate in data analytics is to become familiar with the tools used to analyze and interpret data. Excel is one of the most used tools for data analytics and having a solid understanding of how the tool works now will give you a great base for your future as an accountant. Here are some of the ways that accountants already use Excel:

  • Using spreadsheets to create tabular reports
  • Visualizing data with graphs and charts
  • Filling in templates for budgets and financial statements
  • Importing and integrating data from outside sources to analyze
  • Arranging data in a PivotTable for easy, quick tabulation

If you want to get a jump start on data analytics knowledge, becoming fluent in Excel is key. Plus, Excel is the integrated spreadsheet tool in the CPA Exam, so learning it now will not only help you when it comes to data analytics, but it will help you to pass the exam. Surgent offers an Excel course that will help you become familiar with the software, and it’s accessible as an independent course or comes with the purchase of the Ultimate Pass. Whether you need to brush up on your Excel skills for your current job or you want to take the course alongside a CPA Exam Review prep course, you can find exactly what you’re looking for.

Big data is here to stay, and its importance in the business environment is only going to continue to grow. Learning about data analytics and how data is used in accounting will position you as a valuable asset in the accounting profession.