In recent years, the amount of data available on mobility has grown exponentially. Sensors, connected vehicles and digital technologies generate millions of data points every day on how we move on the roads.
But there is a fundamental question that often remains in the background: when do data truly become useful to improve road safety?
The answer lies not only in technology, but in method.
Having large amounts of data does not automatically mean making good decisions. Especially when it comes to road safety, available information affects sensitive areas such as public policy, urban planning, insurance models and even citizens’ perception of risk.
Without a rigorous approach, the risk is to oversimplify complex phenomena or to turn data into partial — if not misleading — narratives. It is precisely in this transition, from data to interpretation, that the quality of decisions is determined.
This is why road safety cannot rely solely on aggregated statistics or superficial readings. It requires structured, validated and contextualised analyses.
The scientific method is what allows raw data to be transformed into evidence, intuition into knowledge and observation into decision-making. It is the step that gives meaning to numbers, avoiding simplistic interpretations or narrative shortcuts. Applying a method means questioning data in the right way, taking into account the context in which observed phenomena occur. It means considering territorial differences, road usage conditions, real driving behaviours and the time dimension in which events take place.
Only through this integrated reading is it possible to truly understand when and why risk increases, moving beyond generalisations and standardised solutions that often prove ineffective.
Today, telematics represents one of the richest and most reliable sources of data on real-world mobility. But precisely for this reason, it also carries a responsibility: to use those data correctly, transparently and in a verifiable way.
When technology and scientific research work together, data are subjected to models, controls and criteria that ensure their reliability. It is within this balance between technological innovation and methodological rigour that trust is built.
A rigorous approach makes it possible to shift the focus from simply reacting to accidents to preventing risk. This is a fundamental change in perspective, enabling action before damage occurs, rather than merely measuring it after the fact.
Road safety, in fact, is not just a matter of speed or rules, but of context. It varies according to locations, times of day, traffic conditions and driving habits. Understanding these dynamics means being able to design more targeted, more effective and also fairer interventions, because they are calibrated to the real conditions in which risk emerges.
Along this path, building a new data culture becomes essential. A culture that recognises data as a tool, not an end in itself; that technology must serve people; and that the best decisions are born from solid and responsible analysis.
When data are treated with method, they become a collective resource. When method guides technology, road safety stops being an emergency issue and becomes a structural policy.
Because the transition from data to decisions is never automatic: it is a choice of responsibility.