According to App Developer magazine, there has been more data created in the last two years than the previous 5,000 years of human existence. And this makes sense, what with everything that has become digitized, many industries are inherently creating more data - like the energy sector, social media, consumer electronics, manufacturing and automotive.
At this year’s NACE Automechanika show in Chicago, Dan Ricci, global automotive industry cognitive solutions leader with IBM, presented information on data and analytics in the automotive industry, and how the exponential changes in technology will affect our industry in the coming years.
Sure, as a society we excel at amassing large amounts of data; but what do you do with that information? Anything using software is creating data. But, less than 0.5 percent of that data is actually being analyzed for decision making. To utilize this technology most effectively, it’s important to understand all four key steps to fully utilizing this information, according to IBM’s Ricci: Big Data, Artificial Intelligence, Cognitive Learning, and Watson.
For those not familiar, Watson is IBM’s proprietary thinking computer system that is able to review large amounts of data, analyze that data, and make decisions based on that information.
Our first introduction to Watson was in 2011, when it competed on Jeopardy! against the game show’s most successful competitors, Ken Jennings and Brad Rutter. And, Watson blew them out of the water.
Well that’s not very impressive, you might say. But, it really is. Because Watson was not familiar with the categories or questions beforehand. While the engineers pumped Watson full of “data” – facts, figures, historical information, cultural references – it was up to Watson to “listen” to the question, determine an (hopefully correct) answer, and ring in before his opponents. There was no engineer controlling Watson while in play.
There are now multiple iterations of Watson available, adaptable for the needs of different businesses. The goals of this technology are to Understand, Reason, Learn and Interact.
A key point Ricci made during his presentation was that some form of the Watson learning computer technology will be in vehicles soon. Both behavioral and vehicle performance data will be analyzed. While driver behaviors will be the prominent topic for many – the example used: rerouting on a detour and ordering a Starbucks coffee located on that detour so it’s ready when you pass the location, from your vehicle – the main proponent the aftermarket should consider is the performance data. Because this information will aid in diagnosing vehicle issues, and addressing those problems before they actually become issues.
Ricci mentioned IBM has been in discussions with multiple automakers about implementing the Watson technology into vehicle diagnostics. GM already plans to put IBM’s Watson in cars with OnStar Go. The goal, Ricci says, is to create a service technician advisor, a Watson-type program that would take all of the vehicle’s historical repair data to help with diagnostics and repair.
The fleet market is already heavily invested in this technology, because their main focus is uptime. If a vehicle is on the side of the road for any reason, it makes sense they would look to solve an issue before it actually happens by replacing the part or component that would cause the problem.
With the accelerating complexity of vehicles, the diagnosis will be critical in quickly and correctly servicing issues. It’s important to learn about this technology, because it will soon be an aspect of the diagnostic process when a vehicle comes into the shop.
