Electrification solves what powers the vehicle. It does nothing to solve what the vehicle, or the network around it, actually knows. That second problem is the more consequential one, and it is data, not battery power, that will determine how mobility actually functions from here on, writes Vaibhav Kaushik, Co-founder & CEO, Nawgati.
Nearly 58 percent of potential EV buyers in India cite range anxiety as a reason to hold back, according to a Forvis Mazars India whitepaper, and the figure is usually read as a hardware problem: not enough charging points, not enough battery. Ask most people what will define the next decade of mobility and the answer arrives almost automatically anyway: electric vehicles. Battery chemistry, charging speed, range figures have become the shorthand for progress. But swap an internal combustion engine for a battery pack, and you have changed the fuel, not the system. The vehicle still moves through a world of fragmented information: fuel and charging networks that don’t talk to fleets, fleets that don’t talk to traffic systems, traffic systems that don’t talk to city planners.
Electrification solves what powers the vehicle. It does nothing to solve what the vehicle, or the network around it, actually knows. That second problem is the more consequential one, and it is data, not battery power, that will determine how mobility actually functions from here on.
The switch to electric propulsion is easy to see. It shows up in showrooms, charging stations, government targets and subsidy announcements. It is a hardware story, and hardware stories are intuitive: something new replaces something old. The data shift is harder to notice precisely because it isn’t a replacement, it’s an accumulation. Every vehicle on the road generates a continuous stream of information: location, speed, battery or fuel state, driver behaviour, downtime. Every fuel station and charging point generates its own stream: footfall, dispensing patterns, queue times, equipment health. Individually, these streams are operational exhaust, logged, rarely used. Collectively, they are the raw material for a system that can anticipate rather than merely react.
Return to that range anxiety figure. The instinctive fix is to add more chargers, and that matters, but it isn’t the deeper issue. The deeper issue is uncertainty, not knowing whether the charging point ahead is working, occupied, or even real. A driver with a smaller battery and reliable, real-time visibility into charger status will feel more confident than one with a bigger battery and no visibility at all. The battery is a hardware constraint. The anxiety is an information gap.
The same pattern repeats across the mobility stack. Fleet operators lose money not because vehicles are inefficient, but because inefficiency shows up in a monthly report, weeks too late to act on. Fuel retailers lose customers not because stations are badly located, but because there’s no live signal connecting driver demand to station capacity at the moment a decision is being made. The infrastructure is often adequate. What’s missing is the layer that makes it legible, in real time, to whoever needs to decide.
There’s a meaningful difference between collecting data and using it. A sensor that logs fuel dispensed is collecting data. A system that notices dispensing patterns deviating from historical norms and flags a possible equipment fault before it causes downtime is using it. A GPS trace is collecting data. A model that predicts, from thousands of similar traces, where a driver is likely to need fuel or charge next is using it.
This is the shift underway, still mostly invisible to the public conversation about mobility: from data as a record of what happened to data as an input for what should happen next. It changes the posture of the system from reactive to anticipatory. A fuel station that knows demand is about to spike can prepare for it. A fleet manager who sees a vehicle’s behaviour trending toward a breakdown can intervene before it’s stranded. A city planner who can see real, granular movement patterns, not survey estimates, can design infrastructure around how people actually travel.
None of this requires waiting for full electrification or any other headline technology. It requires treating the data mobility infrastructure already generates as an asset rather than a byproduct, building the connective layer between vehicles, fuel and energy points, fleets, and the people making decisions across all three. The vehicles of the future may well run on batteries. But the mobility system of the future will run on information moving as fluidly as the vehicles themselves. Electrification changes what’s under the hood. Data changes whether the whole system can see itself clearly enough to work.