Big data. Data analysis. Data science. But why are they so important? And what does that have to do with the accounting? Accountants use data analytics, and therefore the analytical accounting, to help companies uncover valuable insights within their financial data, identify process improvements that can increase efficiency, and better manage risk.
Accountants are increasingly expected to add value to business decision-making within their organizations and for their clients, both for growth and authority. A solid framework with data analytics gives them the toolset to strengthen their partnerships with business leaders. Let's do some examples.
- auditorBoth those working internally and externally can move from a sample-based model to employ continuous monitoring where much larger datasets are analyzed and verified. The result: less margin for error resulting in more accurate recommendations.
I tax accountants use data science to quickly analyze complex tax issues related to investment scenarios. In turn, investment decisions can be accelerated, which allows companies to respond more quickly to opportunities to beat the competition and the market.
Accountants assisting or acting as investment advisors they use big data to find behavioral patterns in consumers and in the market. These models can help companies build analytical models which, in turn, help them identify investment opportunities and generate higher profit margins.
Four types of data analysis
To better manage big data, it's important to understand four key types of data analytics.
Descriptive Analysis: “What is happening?”
It is used most often and includes the categorization and classification of information. Accountants report on the flow of money through their organizations: income and expenses, inventory counts, sales tax collected. Accurate reporting is a hallmark of sound accounting practices. Compiling and verifying large amounts of data is important to this accurate report.
Diagnostic analysis: “Why did this happen?”
Diagnostics are used to monitor data changes. Accountants regularly analyze variances and calculate historical performance. Because historical precedent is often an excellent predictor of future performance, these calculations are critical to constructing reasonable forecasts.
Predictive analytics: “What will happen?”
Here, the data is used to evaluate the likelihood of future outcomes. Accountants are instrumental in building forecasts and identifying models that shape those forecasts. When accountants act as trusted advisors and create forecasts, business leaders become increasingly confident in following them.
Tangible actions and critical business decisions come from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, to report poor choices.
Prescriptive analytics: “What should happen?”
Tangible actions and critical business decisions come from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, to report poor choices. This insight is an example of the significant impact accountants have in the business world.
Accountants with the extra weapon of big data
Accountants use their technical skills to aggregate information in order to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics is easier for people who already possess quantitative skills.
Accountants are natural problem solvers. Moving from descriptive and diagnostic analytics to predictive and prescriptive analytics requires shifting from an organizational mindset to a curious mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions.
Finally, accountants see the larger context and business implications. The true value of data analytics doesn't come when the data is compiled, but rather when decisions are made using the insights derived from the data. To uncover this information, a data scientist must first understand the business context. And accountants understand and experience this context.
