Fleet professionals must manage their telematics data, or risk becoming overwhelmed.
Like the proverbial two-edged sword, telematics can be a source of great insight or a source of great anxiety. It all depends on how the collected data is managed and the type of goals fleet professionals want to achieve with the help of that data.
It can be tempting for a fleet manager to want to do too much too quickly when investing in telematics. “I’ve seen projects where the client wants to collect all the data possible from each vehicle,” explains Guillaume Poudrier, President of Geothentic. “But at the end of the day, you need to focus on the data that can be most useful. When you grab too much data, it’s going to be more difficult to manage it and to understand it. That’s why I like to go step-by-step: Start with a key objective and then build on that with time.”
Frank Daccardi, Manager, Telematics Solutions at Holman puts his customers at ease by explaining that they don’t have to analyze all the data, all at once. “The data is going to be collected no matter what,” he explains. “When you choose to access it and use it, that’s up to you.”
Daccardi strongly recommends taking small steps and incrementally working towards a bigger goal. “It’s best practice to be very specific and targeted about what you want to do,” he explains. “For example, if you want to improve safety, rather than trying to think about every possible way that you’re going to achieve an end result, maybe you can start with a speeding campaign or a seatbelt campaign, and get everyone used to the fact that your company wants to improve safety. Focus on what you’re trying to accomplish, get comfortable with that, and then work towards the next goal.”
How you implement those goals will determine the success of your campaign, as well as whether any new initiatives will leave you feeling overwhelmed. Tony Candeloro, Senior Vice President, Platform Management & Design at Holman explains: “We tell our clients to think narrow when setting goals, which means they should focus on one area. But for any given goal, think wide. For example, if you’re going to have a speeding campaign, don’t try to deal with everyone who goes five miles over the limit. Start with a wider threshold, get the flagrant abusers, get them under control, and then narrow the parameters over time until everyone complies and you only have to deal with a few exceptions. Otherwise, every vehicle and every driver will be an exception, and you’ll quickly be overwhelmed.”
Monitoring frequency
Another trap some fleet managers fall into is trying to keep an eye on all the numbers in real time. It’s not always necessary to do so. Poudrier says that unless you really need real-time data, you’re better off setting up an alert system that will bring to your attention any anomalies that actually need your attention.
“A well-designed dashboard can be used,” he adds, “and the fleet manager can check it once a day. You can see any exceptions, and you can dig down to get details, if necessary.”
Holman’s Daccardi agrees. “If there’s routing optimization or dispatch, they’re going to look at the data live,” he says. “If they’re focusing on certain initiatives, where they’re reporting on the data reactively, maybe they look at the data on a weekly basis, or maybe that rolls up into a monthly trending report.”
Artificial intelligence
The next step in data management is the employment of AI (artificial intelligence), which can sift through all the numbers and make sense of it. Poudrier says AI will be able to tell fleet managers how to maximize productivity, how to right-size their fleets when taking on a new project, and much more.
AI, he adds, will also be able to change the parameters of fleet policies when necessary. For example, Poudrier explains, your fleet may have an idling policy that’s programmed into each vehicle. After a predetermined number of minutes, the engine may shut off automatically. AI will be able to look at variable such as the temperature outside or the situation the vehicle is in at any given moment, and decide whether to override the policy and keep the engine running. Perhaps doing so is the safest course of action in that particular situation.
Here’s another example of how AI may be able to override fleet policy in order to boost safety: If you’re using telematics to monitor aggressive driving or speeding, you may be alerted whenever one of your vehicles exceeds a pre-set speed threshold. Here again, Poudrier says, AI will be able to look at the variables and make an intelligent decision. Perhaps the road is slippery, or visibility is low due to snow or fog. In this case, the AI may determine that the speed threshold has to be lowered accordingly, in which case it will alert the driver, as well as the fleet manager so that oversight can get involved if the driver ignores the new speed limit threshold.
Holman’s Candeloro adds that AI can be especially useful to help predict maintenance failures. “We’re using analytics to try to predict a component failure, based on diagnostic trouble codes,” he explains. “That’s a very tedious process that can be made more effective and efficient with the use of AI.”
Poudrier stresses that the use of AI with fleet telematics is still in the early stages, and that we’re not there yet when it comes to the full-scale implementation of this emerging technology. However, whenever AI is ready to lend a helping hand, fleet managers will be able to breathe a bit easier, manage more data, and experience a little less stress and anxiety.
