Four myths that could derail your connected data initiatives

Given that auto manufacturers have been collecting data from cars for 20 years, many people believe that they already have all the data they need (or can handle), but this is far from the truth. When it comes to tapping into the much larger revenue and cost-saving opportunities that connected car data offers, data users not only lack the right data at the right time, they’re missing the most effective data strategy to help them find it.

Engineering-grade data helps OEMs combine the right data and data strategy, starting with what problem needs to be solved and then to architecting the best possible data, at the lowest cost. With the right architecture in place, OEMs can monitor vehicle components down to the most detailed vehicle subsystem level at frequency rates up to 50 times what they’re currently doing. This richer, faster, more high-fidelity data translates into deeper mobility insights (i.e. transmission shift metrics and energy consumption), behavioral insights (i.e. seatbelt usage and driving styles), and prognostics insights (i.e. driving emissions and battery health).  

Myth #2: Getting to valuable insights requires capturing all the data.

One of the biggest misconceptions in the connected data world is that we don’t know what we don’t know, so we must capture everything. But, why collect all that data, and incurring the cost to do so, if it doesn’t provide the right information to answer the question? 

To analyse and process vehicle data in real time requires moving only the specific data needed to solve the problem at hand up to the cloud, not all of the data. For example, if an OEM is trying to monitor brake pad use in a vehicle, they don’t also need full data insights into the vehicle’s infotainment system. Edge computing allows you to do exactly this by establishing edge algorithms and triggering rich data capture for anomaly or single status events for standard operations. This allows OEMs to move a targeted amount of data through their system and obtain answers in a fraction of the time and at a fraction of the cost.

Myth #3: Which data to collect must be defined up front.

Technology exists today to configure and reconfigure what data gets collected to avoid being locked into upfront parameters. Auto manufacturers’ data collection strategies can be based on their current business priorities but then refined, optimised and revamped as their strategies change over time.

By updating your data strategy in real time, you can dramatically reduce the amount of time it takes to resolve an issue. In some cases, OEMs have reduced the time it takes to fix an issue by up to 50%—and that kind of reduction translates into some big time savings. This configurability supports not only today’s problem solving but also provides flexibility to update your data strategy to enable future services in the nascent but evolving connected vehicle marketplace.

Myth #4: Connectivity must wait for a new vehicle launch.

For the most part, auto manufacturers wait until each model goes through its scheduled major refresh to incorporate any noteworthy new technology. However, taking this approach with connected vehicles could be a costly mistake. If manufacturers add embedded connectivity to one car model per year over the next five years, about 25% of the cumulative total of vehicles will have connectivity capabilities. But unfortunately, the flipside is also true, that 75% of all the vehicles made over the next five years will not have connectivity capabilities.

Luckily, there is an affordable option today for achieving connectivity - engineering-grade devices that auto manufacturers can plug or wire into late-model vehicles as well as into new vehicles. These devices provide features that drivers want, for example Wi-Fi hotspots, the ability to remote unlock via a smartphone and vehicle diagnostics – all while capturing one-time and recurring revenue opportunities for the OEMs.

So what’s next? Successful auto manufacturers will be those that are able to differentiate with new technologies. Having the right data strategy and the supporting technology in place will provide a path to this future.