Building betters cars through data
Of course, sometimes the best data isn’t the stuff you see, but the stuff that just makes your car better. Cavaretta said Ford analyzes a lot of social media and other external data in order to figure out, for example, what customers are saying about their vehicles compared with other makes and what problems they’re having.
In one recent case, the product development team was curious as to whether the Ford Escape sport-utility vehicle should have a standard liftgate (i.e., it opens manually and the rear window can flip open) or a power liftgate in which the glass and the gate are one piece. In the latter option, the gate opens automatically by tapping under the rear bumper with your foot, but the window doesn’t open at all. Regular surveys hadn’t addressed the question, so Cavaretta and his team took to social media, where people were actually talking about it quite a bit and seemed to heavily favor the power liftgate in most cases. It’s now a feature.
Back in 2004, Ford built a self-learning neural network system for its Aston Martin luxury brand that maintains proper engine function by recognizing engine misfires and particular driving conditions and adjusting warnings and performance accordingly.
Ginder said his team has been improving on that technology ever since and actually expanded its use into a system, called Smart Inventory Management System, that lets dealers ensure they have the optimal stock of vehicles and features on their lots. Historically, he said, some dealers were very sophisticated about inventory management, while others were more reactionary (“They just sold a red Mustang,” he joked, “so they think they need to go order another red Mustang.”) With SIMS, all sorts of data about vehicle sales and other locally relevant data from across the country is aggregated in Ford’s big data platform, and the neural network algorithms learn the current patterns so Ford can make better recommendations — whether or not dealers choose to heed the advice.
Selling big data internally
Cavaretta characterizes the division in which he and Ginder work as “an Ernst & Young, but just for Ford,” an internal consultancy (as opposed to Ford’s more-traditional research and development division) in charge of solving business problems via analytics. About 80 percent of those problems come directly from those lines of business, while about 20 percent are the research division’s own ideas. However, although he’s excited about how big data can help his team answer these questions in novel ways, it’s not always an easy sell with other parts of the company.