The boundless potential of data collection through telematics
I’ve spent the last few months working on a project in the telematics industry, and I’ve learned a lot about this fascinating and increasingly important industry. The term telematics is a combination of telecommunications and informatics. Essentially, we’re talking about using wireless technology to capture information about vehicles. As devices get smaller and cheaper, and vehicles get more high tech, even more data can be easily captured and processed from all kinds of different vehicles. The applications of this technology are similarly boundless. Companies and government agencies are finding many different ways to use this data to capture value. Loss prevention, enforcing higher efficiency standards, and reducing vehicle maintenance are just a few ways this technology can prove valuable. One of the big drivers of growth in the industry has been federal regulations requiring trucking companies to switch to digital logs.
This flurry of growth and demand has led to a quickly growing and evolving industry as companies try to fill the demand for this technology. Vehicle manufacturers are starting to make some data available to customers, and Toyota seems to be leading the charge. There are device-based companies, like Geotab, that send a dongle to plug into your car. Through this device, they are able to capture a much larger dataset than what any vehicle manufacturer currently offers, and make it available via their online portal or API. This is a rapidly changing space. One of the major challenges we ran into was inconsistencies in the amount and shape of data available from various sources. Even seemingly simple things like what constitutes a trip vary from one company to another. One of the companies trying to solve that problem is called Motorq. They work with all the device makers and vehicle manufacturers and can consume data from all of them, offering one consistent API for all vehicles and data sources.
I had a lot of fun on this project, and I learned so much. I’m really interested to see where this industry will go from here. I think the next big developments will be based on machine learning. There are so many ways this data could be used and so much more we could learn. People are already doing work to develop models to help improve fuel efficiency and predict when maintenance needs to be done. I think this kind of work will keep growing and expanding, and it will be very interesting to watch.