Although this article discusses the actual collection and transmission of data, what I find more interesting is how efficiently the vast amount of data is being used in the decision making process for vehicle setup and race strategy. (I strongly believe that data has no value until it is used for analysis and decision making.)

Data coming from the vehicle sensors and other sources are concurrently being used for analysis (by both hundreds of engineers and a wide range of "algorithms") as well as input to simulation models. The results from the simulations means that even more data is being generated even when the vehicle isn't running.

Within minutes, using the data, the different engineering groups (typically responsible for a sub-system, ie. engine, tires, aerodynamics) arrive at conclusions which then the vehicle's race/performance engineers are using to enhance the setup of the vehicle. The results of changes are then fed back to the engineers, and evaluating if the analysis and predictions are correct is a big part of the post-event work.

I honestly can't think of any other industry that carries out this kind of analysis of highly non-linear systems at this scale and speed. The only other industry that I can think of is finance?

Disclosure: I work in motorsports