Guide on How to Leverage Data as a Fleet Safety Tool

Fleet Safety Tool

Most data analysis in the trucking industry focuses on predictive maintenance. However, it can also be used to improve fleet safety performance. This helps managers identify problem areas that directly affect their drivers. From there, they can develop scorecards, rewards programs, and driver coaching that fit the needs of the fleet. Using this type of data analysis for setting up safety programs was the focus of Heavy Duty Trucking’s recent webinar, “Data as a Fleet Safety Tool.”


According to Jeremy Stickling, chief administrative officer for Nussbaum Transportation, 20,000 to 25,000 safety-relevant data points are gathered every week for each truck on the road. In a company like his with 470 trucks, that’s 10 million data points each year. Managing that data requires specialists, specifically in data and IT. He believes that companies that aren’t expanding their IT departments to analyze this data will fall behind.


Doug Marcello, chief legal officer of Bluewire LLC and defense attorney with Marcello & Kivisto, has over 35 years of experience handling truck-related accident cases. According to him, the analysis should focus on data you have access to before an accident. While you can’t predict the location of an accident or who is involved before it happens, you can use your database to identify risky behaviors. This is an important strategy for avoiding “nuclear verdicts,” in which the plaintiff is awarded a settlement far higher than was expected. By using the data you have available to mitigate risk, you can reduce your company’s liability. Fall behind other companies using the same tools, and your company could be charged with negligence.


Good data analysis is an invaluable safety tool for your fleet because it helps you direct driver coaching. According to Marcello, this must be approached carefully. Drivers are likely to reject findings if it’s phrased as “the truck did this.” Instead, drivers should be asked why they see certain results from the data. Behaviors that cause a small increase in risk should be a focus for improvement and rewards, while high-risk behavior should be disciplined.