Council Post: The Data That Used To Explain Car Crashes Now Helps Prevent Them

Amir Hever is CEO and co-founder of UVeye.

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For decades, vehicle data functioned mainly as a record of what’s already gone wrong with your car: a service log, a warranty file or a recall notice mailed out after a defect had already surfaced. Such records could explain a failure, but they rarely arrived early enough to prevent one.

Automotive data, though, is shifting from a historical record into an early-warning signal across the vehicle’s full life cycle, from the factory floor to resale. This may not be headline-grabbing like self-driving vehicles are, but it’s having a real-world impact on the continuous, objective measurement of vehicle condition and its expanding role in catching failures before they reach the road.

Having worked at the intersection of tech and automotive for nearly two decades, I’ve experienced firsthand how data can inform design for the better, helping OEMs improve designs, dealerships better serve customers and fleets prevent breakdowns. Although the four points below trace that path within one industry, each one applies to the broader tech ecosystem: How early does the data surface a risk, and how can these insights close the gap between detection and action?​​

Objective Data At Four Vehicle Stages​

Objective data, not driver behavior or road design alone, is becoming a leading indicator for road safety at four key junctures in a vehicle’s life.

1. Feeding Real-World Wear Data Into Vehicle Design

Automakers increasingly analyze data pulled from vehicles already on the road to see how components perform outside the lab, then feed those findings into future models. In fact, OEMs building unified, AI-enabled quality data pipelines expect to cut warranty costs by 5% to 10% or more, according to McKinsey analysts, while reinforcing quality-by-design in the next model generation.

Whereas it used to take an entire model generation to bake such data-driven safety features into new car designs, that loop now gets closed much faster. Real-world failure patterns inform design decisions in near real time rather than five years later, when the next redesign finally reaches the assembly line.

2. Giving Fleets Earlier Warning Of Tire And Brake Degradation

Waiting for a failure carries a steep, well-documented price. According to FleetNet America and TMC benchmark 2025 data, “organizations lose nearly nine days per vehicle each year to unplanned downtime, costing an estimated $448 to $760 per vehicle per day.”​

To offset this, fleets have started using data-driven monitoring to catch degradation weeks ahead of failure instead of after it. Research from the U.S. Department of Energy found that predictive maintenance programs can eliminate up to 75% of breakdowns and cut maintenance costs by 25% to 30%, turning a roadside emergency, with all the downstream risk to that driver and everyone else on the road, into a scheduled repair.​​

3. Closing The Recall Gap Before Vehicles Change Hands

An estimated one in five vehicles on U.S. roads, 58.1 million in total, still carries an unfixed recall, often for defects serious enough to affect braking, steering or fire risk. Industry-wide VIN-checking efforts have already screened vehicles more than 10 billion cumulative times, helping repair over 30 million open recalls since the program launched in 2018.

The remaining gap is increasingly closing at the point of registration and inspection rather than through a mailed notice an owner can easily miss or overlook. Eight state DMVs, plus Puerto Rico, now check for open recalls automatically during titling, registration or safety inspection, flagging owners instead of leaving it to them to remember. Every vehicle caught at that point is one fewer carrying a known safety defect into its next owner’s hands.

4. Catching Mechanical Failure Before It Becomes A Roadside Incident

Tire-related crashes killed 511 people in the U.S. in 2024, according to NHTSA, underscoring the persistent safety risks associated with tire failure and poor maintenance. But many of the defects behind those crashes are hard to catch on a routine glance: Sidewall damage, uneven wear and slow-leaking punctures rarely announce themselves to the naked eye.​​

Sensor- and imaging-based inspection, trained on millions of data points, can help. Human attention varies with fatigue and lighting, whereas automated visual inspection applies the same standard to every vehicle, every time, catching a category of defects that a routine glance may overlook. The difference between a tire failing an inspection in a shop and a tire failing a driver on the highway can be counted in saved lives.​

Timing, Not Volume, Is The True Metric

The pattern across all four points is the same: The value of data isn’t just how much a company possesses but how early it’s captured, how fast it can be acted on and how completely that action closes the loop before a risk becomes real harm.

As more of the vehicle life cycle becomes measurable, from the factory to the road and at resale, the expectation for road safety should also increase. The leading fleets, dealers and manufacturers won’t just be the ones holding the richest data—they’ll have closed the distance between spotting a risk and acting on it, from the assembly line to the driveway.

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