Finding the truck service needle in the data haystack

Finding the truck service needle in the data haystack

Today’s trucks are generating trailer loads of data, and in the service world that means fault codes.

It’s easy to feel overwhelmed by so much information that you want to ignore it and keep doing what you’ve always done. But then we don’t get any better. So let’s buckle down and take a look at what’s important. 

Today’s trucks are generating trailer loads of data, and in the service world, that means fault codes. Start by focusing on the fault codes that are the most numerous or costly–engine and aftertreatment fault codes are a good place to start. 

While fault codes are often considered headaches, think of them as puzzle pieces. You have to take the time to step back from looking closely at the immediate challenge that you’re trying to overcome to see your big truck service picture. What equipment components are taking up the most space on that puzzle’s canvas–that’s where you can start digging into service data so that you can find a solution to a bulk of your aftertreatment service problems, for example, not just fixing one and then fixing the next and fixing the next without recognizing any patterns that could be causing the problems. 

Most importantly, remember that you’re not alone when trying to identify your truck service trends through data. You can lean on industry service and solutions providers, just like we do here at Data Center. So, Noregon tells us how to determine what data sets you need to be paying attention to.

By actively monitoring the most relevant sets of data, you’re likely to see an increase in efficiency and profit. This will additionally establish confidence in the use of telematics in fleet maintenance and operations going forward. With so much data comes so much potential and can be the most beneficial resource as long as you know how to use it. 

Fleet Equipment’s Data Center is sponsored by Noregon. Subscribe to our newsletter to catch every episode as we’ll be diving into use cases, talking with the data pros and making data usage approachable.

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