Why the OEM versus device-level telematics debate misses the point for mixed-fleet operators

A fleet manager overseeing 3,000 vehicles from a dozen manufacturers is running twelve parallel data environments that happen to share a yard, a maintenance budget and a P&L.

Each OEM sends data on its own schedule, in its own format, using its own taxonomy for describing the same events. A battery fault in a Mercedes looks different in the data stream to a battery fault in a Renault. Fuel level is reported at different intervals. Trip start and end points are calculated differently. Before any of this reaches a dashboard or informs a decision, something has to make it consistent.

That is the real challenge of the multi-OEM fleet. The industry has been slow to address it, partly because the debate got turned into a binary.

OEM data versus device-level data is the wrong frame

Most discussions about connected vehicles assume fleet operators face a trade-off: OEM-native data on one side, aftermarket telematics hardware on the other. Better to go native, the argument goes. Fewer devices, less installation cost, data straight from the source.

OEM data and device-level telematics data do not do the same things. They serve different use cases.

A rental fleet needs fuel level, mileage and a fault flag. That information is available directly from the OEM, already calculated, accurate enough for the purpose. An urban logistics operator running tight delivery windows needs position data every few seconds and duty cycle analysis across a twelve-hour shift. OEM data, which may ping every twenty seconds or update at journey end, was not designed for that.

In the first scenario, OEM data is accurate and sufficient. In the second, it was never designed for the job.

The platform problem

There is a subtler issue that does not get discussed enough. Most telematics platforms were built around one data model. When OEM data arrives in a different format or at a different frequency, the platform treats it as an exception.

Instead of using the battery state of health value the OEM already calculates and transmits, the platform tries to derive that figure from raw charging and discharging data that is either unavailable or less precise. Instead of reading the odometer values at journey start and end that the OEM provides directly, the platform calculates distance from GPS position points that introduce their own margins of error.

The OEM data is there. It is simply not being used.

What harmonisation actually means

For a fleet running multiple brands, the question is whether the platform can work with both data sources, consistently, at scale.

Harmonisation in practice means that a fuel reading from a Volkswagen and a fuel reading from a BYD arrive in the system using the same unit, the same reference point and the same update logic, before any report is generated or alert is triggered. It means that battery health, fault codes and trip data are interpreted against a common framework regardless of which manufacturer produced the vehicle.

For a fleet operations team making daily decisions about maintenance scheduling, driver assignment or vehicle deployment, inconsistent data is a compounding problem. Good analytics on inconsistent inputs does not produce good decisions. It produces confident guessing.

The fleet that has this working

The operators who have got this right are not running OEM data in one system and device data in another. They are running a platform that uses both, applies each to the use cases it serves well, and presents the result consistently across every brand in the fleet.

For European fleet operators managing increasingly mixed assets across multiple countries, this is where operational clarity comes from. The fleet that can answer consistent questions about any vehicle, from any brand, across any market, is working from a foundation the others are still trying to build.