Getting data is easy, making sense of it is another story

With Andrew Rossington, Chief Product Officer, Teletrac Navman

We came to many interesting realizations during the pandemic. Consider that we now know that workers can be productive at home, corporate information can be accessed safely and securely from anywhere, and home deliveries can scale in a hurry. The latter wouldn’t be possible without the proliferation of telematics through sensor technology, mobile-based data collection and, more recently, artificial intelligence (AI). It would have been difficult to believe that we could have all of this machine generated data at our fingertips a decade ago. Knowing exactly what to do with it all can still be a challenge.

Take, for example, this scenario: As a fleet manager you’re pushed for time to get deliveries out and you have drivers with limited available driving hours and a finite amount of equipment to ship the goods. Now what? Use the data sets available to you in real-time to calculate who is the best driver, vehicle and route combination to get said deliveries out, making the most of the available resources based on the data you have.

First, you have to thinks about your business objectives. Are you looking to reduce fuel costs, become a safer fleet, hold drivers and/or customers accountable, set smarter prices, map out the best routes — or all of the above? Knowing your business objectives will help set the course for use of a telematics data system. The data from the telematics system will be an invaluable asset in reaching your business goals and can then help your fleet become more competitive, by winning and renewing more contracts, attracting and retaining the best drivers, and by making compliance a practically automated endeavor. So, how do you do it? The answer is all in the data, which will help you to expose inefficiencies, identify positive anomalies which can drive you to take knowledgeable actions in real time.

Identify inefficiencies
Even with an abundance of raw information, it can be tough to know what the data means without something to compare it to or help ‘connect the dots’. That’s where AI coupled with Machine Learning creates predictive analytics and historical data to highlight certain data points which can guide fleet managers as they review and make decisions.