There are four types of data analytics models which are descriptive, diagnostic, predictive, and prescriptive analytics. Richardson et al. diagnostic analytics explains why something happened; predictive analysis predicts what is likely to occur in the future, and prescriptive analytics looks at what has happened, why it happened to help comes up with a course of action. The use of these different approaches depends mainly on the company's need, whether to help reduce costs, gain greater insight into target markets, or decide which sectors deserve their resources pulled. However, the best category to utilize management presentations is prescriptive analytics because it combines the other three approaches.

Descriptive analytics only provides a summary of the dataset. The purpose is only to describe what has happened. An example of descriptive analytics is Quickbooks' business overview, where the dashboard shows total monthly expenses, monthly sales, open invoices, overdue invoices, and so forth.

The diagnostic analysis explores in more detail to understand why something has happened. The primary reason for diagnostic analytics is to identify outliers within the datasets by comparing them to a benchmark. An example of diagnostic analytics is budget vs. actual report.

Predictive analytics is focused on predicting and understanding what could happen in the future. Since predictive analytics can tell a business what could happen in the future, this methodology empowers executives and managers to take a more proactive, data-driven approach to business strategy and decision-making. For example, companies can use predictive analytics to forecast customer behavior and purchasing patterns to identify sales trends.

The final type of data analysis is the most sought-after, but only some organizations are truly equipped to perform it. The prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.