Question

2. How can accountants use data analytics to improve business processes? In each forum, you will select a question and post a new thread answering that question in at least 300 words.

Answer 1

Accountants use data analytics to help businesses uncover valuable insights within their financials, identify process improvements that can increase efficiency, and better manage risk. “Accountants will be increasingly expected to add value to the business decision making within their organizations and for their clients,” comments Associate Professor Wendell Gilland, who teaches Data Analytics for Accountants at UNC Kenan-Flagler Business School. “A strong facility with data analytics gives them the toolset to help strengthen their partnership with business leaders.”

Here are a few examples:

Auditors, both those working internally and externally, can shift from a sample-based model to employ continuous monitoring where much larger data sets are analyzed and verified. The result: less margin of error resulting in more precise recommendations.

Tax accountants use data science to quickly analyze complex taxation questions related to investment scenarios. In turn, investment decisions can be expedited, which allows companies to respond faster to opportunities to beat their competition — and the market — to the punch.

Accountants who assist, or act as, investment advisors use big data to find behavioral patterns in consumers and the market. These patterns can help businesses build analytic models that, in turn, help them identify investment opportunities and generate higher profit margins.

Four types of data analytics

To get a better handle on big data, it’s important to understand four key types of data analytics.

1. Descriptive analytics = “What is happening?”

This is used most often and includes the categorization and classification of information. Accountants report on the flow of money through their organizations: revenue and expenses, inventory counts, sales tax collected. Accurate reporting is a hallmark of solid accounting practices. Compiling and verifying large amounts of data is important to this accurate reporting.

2. Diagnostic analytics = “Why did it happen?”

Diagnostics are used to monitor changes in data. Accountants regularly analyze variances and calculate historical performance. Because historical precedent is often an excellent indicator of future performance, these calculations are critical to build reasonable forecasts.

3. Predictive analytics = “What’s going to happen?”

Here, data is used to assess the likelihood of future outcomes. Accountants are instrumental in building forecasts and identifying patterns that shape those forecasts. When accountants act as trusted advisors and build forecasts, business leaders grow increasingly confident in following them.

4. Prescriptive analytics = “What should happen?”

Tangible actions — and critical business decisions — arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, raise an alert on poor choices. This insight is an example of the significant impact that accountants make in the business world.

Why accountants make excellent data scientists

Accountants have outstanding technical skills. Gilland notes, “Accountants are used to aggregating information to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics comes more easily to people who already possess excellent quantitative skills.”

Accountants are natural-born problem solvers. The jump from descriptive and diagnostic analytics to predictive and prescriptive analytics requires that one shift from an organizational mindset to an inquisitive mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions. Accountants are experts at making this jump.

Accountants see the larger context and business implications. The true value of data analysis comes not at the point when the data is compiled, but rather when decisions are made using insights derived from the data. To uncover these insights, a data scientist must first understand the business context. Not only do accountants understand this context, they live it.