In the past decade, everyone has been excited about all kinds of moving and shaking in the car industry. From the impossibly expensive Tesla electric car to the realization of the Chevy Volt and its subsequent flop, consumers and carmakers alike are still cautiously optimistic about the future of automotive technology.
Since the advent of fuel injection in the 1970s, cars have had some kind of computer on board—granted, those "computers" in the 1970s weren't anywhere near what they became in decades moving forward. Nowadays, cars are loaded with copious computers—from ways to predict the terrain to predicting when to brake, and from weather and GPS controls to computers that give enormous amounts of data about the engine and other moving parts to keep motorists and passengers safe.
With all the changes cars face, onboard computers and high-end technologies will only become more complex, generating more and more data. Take the example of the self-parking car. Volvo is currently working on a self-parking car and plans to release some features of what Volvo safety advisors are calling "autonomous steering"—a feature that will allow the 2014 or 2015 Volvo XC90 to detect and drive itself into spaces like a giant Roomba.
Self-parking creates and uses an enormous amount of data But there's more to big data in self-parking cars than how much data is being created—it's how much and how well data is being used that's important, and this is were big data companies will be competing.
The self-parking Audi A7 uses data from sensors, but it also uses data history left behind by the human driver to generate choices about how to maneuver, taking the place of a human driver's intuition. The A7 also works by way of an app that interacts with its human "driver," making it a self-driving car insomuch as it can drive to a storefront to meet a shopper laden with grocery bags, and avoid curbs and pedestrians all the while.
Once self-parking and self-driving cars become the norm in vehicles, the use of big data won’t be the question—reworking past versions of self-driving technologies as glitches are discovered and fixed will be. Decisions about how to rework such glitches will be based largely on raw data collection from anecdotal events once they occur with great enough frequency to know that accidents caused by the same irregularities are not isolated events.
We now know that big data warehousing is easier than ever, and with companies generating entire petabytes of data every minute of every day, the issue isn’t space, it’s human interaction with these tomes of data that will mean real, usable business intelligence that will pave the way to a completely self-directed, self-driving car. None of the data collected will mean anything if it cannot be applied effectively, and that means a whole new industry within the industry of automotives, one which will require new skill sets and a new kinds of education and training for future data analysts working for carmakers.
Big data, once applied successfully, will change how self-parking and self-driving works to avoid accidents and other robot foibles, like going to a wrong address, over or underestimating the time it will take to get there, or accidentally taking off without passengers. Using data from the past to shape the future is the best way big data will be useful in creating safety and efficiency in the automotive industry moving forward.
Article Written by Thomas Gibbs of the Marketing Robot. Follow Thomas on Twitter @captain_TOM_T for more marketing tips and updates.