The automotive world benefits from data in so many very important ways.
From the development departments of a manufacturer to the retail outlets where you buy their stuff, and from the developers at a professional race team's headquarters to the driver and race engineers at the track, they all rely very heavily on good data.
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Data is basically just quantifiable information. The bigger the data set, and the more things it is compared to one at a time (with as many variables controlled as possible each time), the more reliable that data is. That's because you then know how repeatable that outcome is, having reduced the possibility that an outcome was the result of chance alone.
Gathering data in the right way to attain a very high level of reliability and repeatability is called the scientific method, and it's how, for instance, researchers studying brain development were able to determine that rats raised in an enriched environment with lots of things to play with could be taught to drive, whereas those raised in a clean but unstimulating environment were not.
Data is only as good as its interpretation though, and it helps to know what the surrounding circumstances are. It's why statistics can be twisted so easily; they're just like a quote that's been taken out of context because their meaning can get lost.
Stand-up comedian Don McMillan has some fun with this in one of his routines that you can catch on Dry Bar Comedy. One of his jokes about the other side of statistics refers to 95 per cent of car accidents happening within one mile (1.6km) of home, and his first punchline about it is "there's always a couple of people thinking, 'ooh, I got to move'. No, that's not going to help," he says shaking his head with a facepalm, "trust me, that is not going to help."
So the correct interpretation of data clearly matters, but when it is applied to the same circumstances (context), good quality data is incredibly useful.
As just one example, for decades now tyre manufacturers have been not just road testing but torture testing their tyres to find the construction method, mix of materials, and tread pattern they think is best to ensure they perform well and are safe under all sorts of extreme conditions. And data gathered from testing is also how we come to get a tyre rating system for performance factors such as wear, grip and maximum safe speed.
The collection of data is how we come to have numbers for road incidents and road deaths, and it is investigations into the more serious of these events which give us the context, the cause, of each one. The action or inaction that was most attributable to the crash occurring or the severity of the injuries, if it keeps occurring or seems likely to occur again or a solution is just easy to implement, then that's how we prevent it.
This is how we came to understand that speeding on the street, drink driving and driving while tired are all extremely bad ideas that should be discouraged through laws and awareness campaigns. And it's how we know that seatbelts can save lives.
We also know that just because we can't see or detect something harmful in the air, it doesn't mean it's not there. It's why canaries used to be sent down coal mines for instance; to test if the air was still breathable.
This inadequate sense we have for the breathability of air is also why gaseous fuels like LPG (usually a mix of propane and butane for automotive use) are fragranced with a distinct and unpleasant odour. Without it you'd have no idea you were breathing it in.
We're also just as bad at detecting lethally-poisonous concentrations of carbon monoxide, one of the waste products in combustion engine exhaust fumes, which is why it's so easy to succumb to exhaust fumes in a confined space.
So if data is inherently useful when used appropriately, and we can't trust our senses to identify many of the harmful things that could be in the air we breathe in or out, then how can anyone argue against the idea of wearing masks to help control the spread of a pandemic? The short answer is, you can't.
