Accountants use data analytics to help businesses uncover valuable insights into their finances, identify process improvements that can increase efficiency, and better manage risk. Data analytics for accountants is important for anyone working in the finance-accounting field.
How important is data analytics for accountants?
“Accountants will increasingly be expected to add value to business decision-making within their organizations and for their clients,” says Associate Professor Wendell Gilland, who teaches data analytics for accounting at the UNC Kenan-Flagler School of Business. “A strong foundation in data analytics gives them a toolkit that enhances their collaborative relationships with business leaders.”
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Some examples of the role of Data Analytics in accounting
Auditors, both internal and external, can move from sample-based models to continuous monitoring where larger data sets are analyzed and verified. The result: lower error rates leading to more accurate recommendations.
Tax accountants use data science to quickly analyze complex tax questions related to investment scenarios. In turn, investment decisions can be accelerated, allowing companies to react more quickly to opportunities to beat competitors and markets before they change.
Accountants who support or act as investment advisors use big data to uncover patterns in consumer and market behavior. These patterns can help businesses build analytical models that help them identify investment opportunities and generate higher returns.
Types of Data Analytics for Accounting
Types of Data Analytics for Accounting (Image Source: Internet)
To better handle big data, it is important to understand the four main types of data analytics for accounting below:
2.1 Descriptive analytics = “What is happening?”
This is the most commonly used and involves classifying and categorizing information. Accountants report on the flow of money through their organization: revenues and expenses, inventory levels, sales tax collected. Accurate reporting is a hallmark of sound accounting practices. Compiling and verifying large amounts of data is crucial to this accurate reporting.
2.2 Diagnostic analytics = “Why did this happen?”
Diagnosis is used to track changes in data. Accountants regularly analyze variances and calculate past performance. Because historical precedent is often a good indicator of future performance, these calculations are essential to building sound forecasts.
2.3 Predictive Analytics = “What will happen?”
Here, data is used to assess the likelihood of future outcomes. Accounting is instrumental in building forecasts and identifying the patterns that shape those forecasts. As accountants act as trusted advisors and build forecasts, business leaders become more confident in following them.
2.4 Prescriptive Analytics = “What will happen?”
Tangible actions and important business decisions arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations about future growth opportunities or, in some cases, to warn against poor choices. This insight is an example of the significant impact accountants have in the business world.
Why Accountants Make Great Data Scientists
Accountants have exceptional technical skills. “Accountants typically synthesize information to create a picture of an organization that summarizes the details of each transaction,” notes Gilland. “Descriptive, predictive, and prescriptive analytics are easier for those who already possess strong quantitative skills.”
Why Accountants Make Great Data Scientists (Image source: internet)
Accountants are natural problem solvers. Moving from descriptive and diagnostic analytics to predictive and prescriptive analytics requires a shift from an organizational mindset to a curious one; a shift from sorting and organizing information to figuring out how to use that information to make important business decisions. Accountants are experts at making this leap.
Accountants see the larger context and business implications. The true value of data analytics comes not when the data is aggregated, but when decisions are made using the insights gleaned from the data. To uncover these insights, a data scientist must first understand the business context. Accountants not only understand this context, they live it.
