Fleets have more telematics data than ever, but many still run it through three disconnected dashboards instead of one model that informs decisions.
Consider a mid-sized regional carrier's safety director analyzing the telematics interface on its dashboard today. The dashboard registered more than 40 alerts due to harsh braking, out of which only two or three of them produced some valuable insights.
The trucks are instrumented fine, and nothing was wrong with the sensors. What that safety director needed was a layer that would have told them which two alerts out of forty were worth a phone call before they opened the dashboard.
This is surprisingly very common across fleets that have invested seriously in telematics. Data isn't the shortage anymore. GPS pings, harsh-event flags, idle time, fuel burn, fault codes, HOS records all stream in cheap and constant, and "how much are we collecting" stopped being the interesting question years ago. What many fleets never built is the layer underneath all of it, something that lets a safety director, a sustainability lead and a dispatch manager work from one reconciled model instead of three logins that don't agree. If you treat safety, sustainability and efficiency as three separate reporting problems, then you end up with three separate, half-useful answers to what is really one question sitting inside one dataset.
The alert queue is not insight
A harsh-braking event fired without lane context, weather data, load weight or time of day is a data point, not a diagnosis. Most telematics platforms are tuned to flag events against a fixed threshold, and fixed thresholds generate a lot of static on a wet mountain grade and very little signal on a flat, dry interstate. Fleets that don’t move past the raw alert feed end up training their safety teams to triage volume instead of investigate risk. I’ve watched this tension push good safety staff into administrative work instead of risk reduction.
A lot of the industry conversation about telematics gets this backwards. It jumps straight to hardware, looking at better sensors, more cameras and tighter GPS accuracy. In my experience, hardware isn't usually where the value gets lost. GPS units and ELDs are commodity-grade reliable at this point. The value gets lost downstream in how the data gets reconciled once it lands. Even fleets that aren't touching AI yet are running into this. Fleet Advantage's 2026 survey on AI adoption found data integration is now the top barrier fleets report, cited by 71%, up from 38% the year before, with inaccurate data close behind at 65%, nearly triple where it stood in 2025. Those numbers are measuring AI readiness, but the root cause they're pointing at is the same one showing up in a basic dashboard triage problem: nobody reconciled the data before asking anything of it. Buying a better sensor won't fix that because the sensor wasn't what failed.
One feed, split three ways
Trucking's overall cost per mile hit $2.336 in 2025, the highest point in ATRI's decade of tracking it, and every line item on that P&L is a candidate for the same reconciliation work. A better dashboard won’t fix this on its own. But treating GPS, ELD, fuel and maintenance feeds as inputs to a single reconciled model, instead of three vendor silos that each answer one question, will. The same trip-level data that flags a harsh-braking event also carries idle duration, route deviation and fuel burn for that stretch of road. Modeled together, hard braking that clusters on a specific lane, at a specific time of day, with a specific load profile, tells you something a raw count couldn’t. It might point to a routing fix rather than a coaching conversation or show that your efficiency lane is actually your most expensive lane once you price in the fuel burn from repeated hard stops.
Fuel remains one of the largest single line items on that P&L, averaging close to 48 cents per mile industry-wide last year. Maintenance is not far behind, up 8.6% to $0.215 per mile in the same period, which is exactly the kind of line item a reconciled model should be catching before it becomes a trend rather than after. In demand planning work, I push the same rule. Reconcile the sources first, then trust what gets built on top of them.
Where these programs stall
The problem, which isn’t bad models, is the data that wasn’t structured to be modeled in the first place. ELD data lives in one vendor's schema, fuel card data in another, maintenance records in a third, and reconciling vehicle IDs, driver IDs and trip segments across all three never becomes anyone's job. Analytics teams end up rebuilding the same joins every quarter instead of running new analysis. The other stall point is drift. A scoring model tuned on last year's lane mix and last year's fuel prices degrades as routes and diesel costs shift, and the slide goes unnoticed until the numbers stop making sense to the people using them.
The platform layer helps, but it is not the first decision
Unifying environments can make the reconciliation work faster once you know what you are reconciling and why. But buying a platform before deciding which questions your safety, sustainability and efficiency teams actually need answered in the same breath just moves the silo problem into a more expensive tool.
Point the next dollar at the actual gap
Most fleets cannot answer three basic checks honestly. Safety, sustainability and efficiency leads are pulling separate reports from separate vendor portals instead of one shared dataset. Tracing an alert back to route, load and driver context takes a data request instead of a minute. And checking whether scoring thresholds still hold when fuel prices or lane mix shift never becomes anyone's job either. That is where the next investment belongs, not more sensors and not another dashboard.
Fixing this is mostly an organizational move. Someone has to own the reconciliation work across vendors, a job that sits open at most fleets right now. Once that ownership exists, telematics turns into a planning asset, with safety, sustainability and efficiency all reading off the same numbers for the first time.
