Debunking the biggest myths about telematics from the people who work with it every day.
If you work in data, artificial intelligence and connected mobility, sooner or later someone will ask you the same question:
“So… do you always know where people are?”
It’s a fair question. But it only tells a small part of the story.
Location is one of the data points available to us, but it is far from being the purpose of our work.
To us, a journey is not a blue line moving across a map. It is a sequence of events that provides context: the type of road, traffic conditions, time of day, driving behaviour, acceleration, braking events and interactions with the surrounding environment.
On its own, a single data point tells us very little. Its real value comes from interpreting it, connecting it with millions of other events and transforming it into knowledge. In other words, we are not interested in where you have been. We are interested in understanding what every journey can teach us.
That knowledge enables insurers to understand risk more accurately, build more sophisticated pricing models and develop services that put prevention at the centre.
Imagine two drivers.
They drive the same route to work every morning.
They own the same car.
They live in the same neighbourhood.
At first glance, they appear almost identical.
Yet one spends most of the journey on congested urban roads, while the other mainly drives on free-flowing rural roads. One always drives during rush hour, while the other travels when traffic is light. One navigates busy intersections every day, while the other follows a much simpler route.
The difference is not where they start. It is their exposure to risk.
That is where telematics becomes truly interesting.
A single harsh braking event is not enough to label someone a “bad driver“. One isolated event tells us very little. It’s a bit like trying to understand an entire conversation by listening to just one sentence. It is the analysis of driving behaviour over time, together with the context in which events occur, that makes it possible to understand risk far more accurately.
At this point, another question usually follows.
“So… what do you actually do?”
The honest answer is that we spend far more time validating data, comparing models, testing algorithms and identifying patterns across millions of journeys than looking at maps.
Because a map tells you where a journey happened.
Our job is to understand what that journey can teach us.
One last confession.
“Telematics professional” is not really a job title anymore.
Today, behind that word you’ll find data scientists, AI engineers, mathematicians, software developers, mobility experts and data analysts. Different backgrounds, different skills, one common goal: transforming data into knowledge.
So yes, we’ll probably keep calling ourselves “telematics professionals”.
Mainly because “Confessions of a Data Scientist Specialising in Predictive Models for Connected Mobility” would have been a ridiculously long title.
Confessions of a Telematics Professional is a new OCTO series that explores the most common myths surrounding telematics and explains how data, artificial intelligence and connected mobility are reshaping the insurance industry.
By Adriana Zambon