Transportation Big Data is a Big Target
n this special guest feature, Neil Cawse, CEO of Geotab, looks at the big data industry from the lens of vehicular/transportation big data, a sprawling category that includes location, speed, drive train diagnostics, fuel economy and driving behavior. Neil is an electrical engineering graduate who has been starting and running his own businesses since 1992. These companies include Vircom – a software development company sold to Datatec in 1998 which had over 100 software engineers when sold. He started Geotab Inc. when moving to Canada in 2000 and built the company from nothing to now being in the top 50 fastest growing tech companies in Canada and top 200 in North America. Geotab Inc. is a family business. Neil at his heart is an engineer and is involved day to day in the engineering decisions and work. Neil prides himself on the company’s integrity and its forward thinking ideals – including making sure the customer gets the right solution ahead of the company’s profits.
Big data generates a lot of buzz because it enables a lot of deep insights that otherwise would be difficult or impossible to glean. That means it’s valuable not only to businesses and other organizations, but also to hackers and fraudsters.
Take the example of vehicular/transportation big data, a sprawling category that includes location, speed, drivetrain diagnostics, fuel economy and driving behavior. Vehicle data is typically collected via the On-Board Diagnostics (OBD) port, which is built into every U.S. manufactured light duty car and truck since 1996. The data is then relayed, often over cellular, to the vehicle owner and/or a party it authorizes, such as its insurance provider.
This data gives fleet owners, consumers, government transportation departments and other stakeholder valuable information such as:
Hard acceleration and braking. For example, a taxi company might use this data to identify employees who need a refresher course that increases safety while reducing premature brake wear. Parents, meanwhile, could use this feedback to determine whether their teens are driving recklessly. State, federal and municipal transportation departments could use anonymized, aggregated versions of this data to identify roads that need to be redesigned.
Health of systems and components. A trucking company can use a remote diagnostics system to collect engine and transmission fault code information and identify when certain repairs are needed. Through analysis and proactive maintenance, companies can save money and keep their trucks on the road.
Congested areas. There are plenty of sources of real-time traffic information, but big data can be more effective for avoiding traffic jams and all of the costs that come with them. Understanding where their vehicles operate well below the speed limit helps long-haul trucking companies determine which routes their drivers should avoid, thus maximizing productivity and minimizing fuel waste. Transportation departments can use similar data to identify areas that need to be upgraded.
Safety and fuel efficiency. Federal regulators could use anonymized, aggregated data to verify automaker claims about fuel efficiency and to identify models that are experiencing high amounts of accidents, potentially indicating a design flaw. This information then could be shared with consumers and fleet owners, enabling them to make decisions that would be difficult or impossible without those big data insights. With regards to fleet safety, the data can be used for driver risk and safety reporting, which captures information on speeding, seatbelt usage, driver braking habits, acceleration, after-hours vehicle usage, and more.
Security Doesn’t Have to Come at the Expense of Opportunity
Those are just a few examples of vehicular data’s economic benefits, both for individual vehicle owners and collectively as a society. These same benefits are jeopardized more by attempts to lock down the data than by breaches.
Make no mistake: This data must be kept secure and private, and it can be in a variety of technological and policy ways. For example, the end-to-end encryption used for other types of big data can be applied just as effectively. Meanwhile, fleet owners, insurance companies and consumers can determine which data they’re willing to share, with whom and how it can be used.
These are among the sensible, practical and effective alternatives to heavy-handed laws, such as the SPY Car Act of 2015, that would severely limit who can access OBD-facilitated data – and thus eliminate many of that data’s benefits for vehicle owners. A more balanced approach can be found in “right-to-repair” legislation, which ensures that the OBD-enabled diagnostic data that’s always been available to manufacturer dealerships is accessible by independent repair shops, too. As a result, owners have more options for servicing their vehicles, as well as the information they need to service vehicles themselves.
That’s yet another example of the economic benefits that vehicular data provides, and we’ve only scratched the surface of what’s possible. To continue that innovation and drive even more benefits, regulators, vehicle owners and the automotive ecosystem should always look for ways to balance privacy and security with utility and opportunity.