While often discussed together, predictive analytics and traditional business intelligence (BI) answer different questions.

Business intelligence primarily focuses on descriptive analytics, analyzing past data to answer 'What happened?'.

It uses historical data to create dashboards and reports on past performance.

Predictive analytics, on the other hand, focuses on the future, answering 'What is likely to happen?'.

It uses the same historical data but applies advanced techniques to forecast future trends and behaviors.

The process involves building a predictive model.

This model is trained on a set of historical data and then used to generate predictions on new data.

For example, a BI report might show that fuel consumption increased by 10% last month.

Predictive analytics would analyze the underlying data (routes, driver behavior, vehicle type) to predict which vehicles are most likely to have high fuel consumption next month, and why.

In the context of fleet management, predictive analytics is the overarching discipline that enables capabilities like predictive maintenance, ETA prediction, and driver risk scoring.

It transforms data from a rearview mirror into a forward-looking guide, enabling proactive, data-driven decisions that optimize costs, efficiency, and safety.