How fleet data gets you from what happened to what to do next

Vehicles in a connected fleet report engine faults, braking, idling, location and fuel use. Add cameras, and they also capture what happens on the road. Across a few hundred vehicles, that adds up to thousands of data points a day.

Most of that data ends up in a report. Very little of it tells anyone what to do next.

Turning those reports into decisions depends on the quality and scale of the data behind them.

Data tells you what happened

Telematics data is a record of what your vehicles did. It tells you a vehicle idled for 40 minutes, a driver braked hard on Route 9 or a fault code appeared on Tuesday. That's useful for compliance, billing and looking back at an incident.

What it can't tell you is what any of it means. Is the idling a habit? Is the driver who braked hard getting riskier? Will Tuesday's fault code lead to a breakdown next week?

To answer those questions, you need to compare each event with everything else known: the road, the driver's history, the vehicle's maintenance record and how similar vehicles have behaved. That's what turns data into intelligence. You have the foresight to know which vehicle to service, which driver to coach or which route to change.

Better data means better predictions

Four things make fleet data good enough to predict from:

• It's recorded precisely. A reading taken every few seconds can miss what happened between readings. Logging each meaningful change in speed and direction captures the details that explain an event, such as a hard brake or a sharp turn.
• It's consistent across sources. A mixed fleet can have dozens of makes, models and model years, along with data from devices, vehicle manufacturers and other sensors. Putting it all in the same format, and catching bad readings or dropped connections, means you can compare one vehicle with another.
• It includes real-world context. Hard braking on a highway means something different from hard braking in a school zone. Road type, speed limits, traffic and weather show which is which.
• There's enough of it. A single fleet might see a handful of breakdowns a year. A model that learns from millions of vehicles has seen the same failure patterns across many makes, routes and conditions, so it can recognize them earlier.

For data and IT teams, this decides whether fleet data produces reliable results in predictive models, reporting tools or your own AI projects, or needs a lot of cleanup first. For a closer look, read The AI tools are ready. Is your data?

Three kinds of fleet intelligence

Fleet teams use intelligence to answer different questions. Three of the most common are below.

Actionable intelligence: What should we act on now?

Actionable intelligence turns a pile of alerts into a list of what to deal with first.

Example: An AI dash cam detects a driver looking at their phone while following too closely. An in-cab alert prompts the driver to correct the behavior in that moment. The event is ranked by severity, so the safety manager starts with the riskiest events instead of scrolling through all of them.

Predictive intelligence: What is likely to happen next?

Predictive fleet intelligence spots patterns that point to a problem before it shows up.

Example: A van isn't due for scheduled service for another two months. Its battery voltage has been dropping in a pattern that, across millions of similar vehicles, usually comes right before a vehicle won't start. Predictive maintenance identifies it for service now, before it strands a driver.

Prescriptive intelligence: What should we do?

Prescriptive intelligence recommends what to do based on how your vehicles are used.

Example: Trip data shows which vehicles run routes that an electric model could cover on a single charge, along with the projected fuel and emissions savings. The fleet gets a ranked list of which vehicles to replace with EVs.

What it looks like across fleet operations

Fleet intelligence supports the work fleet teams do every day:

• Maintenance and uptime: See which fault codes are most likely to lead to a breakdown, and catch some problems before a warning light comes on, so repairs happen on your schedule. Follow each repair from start to finish to see where vehicles sit waiting in the shop and get them back on the road sooner. Our guide How to make maintenance your fleet's competitive advantage covers the move from reactive to predictive maintenance.
• Driver safety: A machine learning model predicts which drivers are most likely to be in a collision, including drivers with a clean scorecard. Coach them using video of the specific events, where cameras are installed. The Unified Safety Solution guide covers how telematics, video and predictive risk work together.
• Routing and dispatch: Plan routes around labor costs, fuel, traffic and how much each job is worth, and adjust when conditions change during the day.
• Daily operations: Type a question the way you'd ask a colleague, such as "Which vehicles idled the most this week?", and get an answer from live fleet data in seconds, without building a report.

Because every area draws on the same data, what you learn in one can change a decision in another. A vehicle identified for maintenance can be pulled from tomorrow's route without anyone checking two separate systems.

Where Geotab Intelligence fits

Geotab Intelligence is the data and AI layer across the Geotab platform, drawing on one of the world's largest and cleanest commercial fleet datasets. It includes the data Geotab collects from its own devices, vehicle manufacturers and sensors, along with the predictions built on that data for maintenance, safety and operations.

The models behind it are built on:

• 6M+ connected vehicles
• 37T+ data points annually

Geotab also brings 25+ years of fleet expertise to interpreting what that data means on the road.

Teams can use that intelligence inside MyGeotab, ask questions in plain language with Geotab Ace, connect live fleet data to AI tools such as ChatGPT or Claude through the Geotab MCP Connector, or send data to reporting tools and their own models through APIs and the Geotab Data Connector.

The platform is backed by ISO 27001 and SOC 2 Type II certifications, and Geotab's AI practices align with the NIST AI Risk Management Framework.

Learn more about Geotab Intelligence