I became fascinated with the power of managing data back in 1980 when I worked for the Wall Street Journal—a publication that was built from its founding to distill complex business information and turn it into actionable intelligence for investors. I became obsessed with the power of data when I started Vigillo in 2007 as a means to help safety departments collect and analyze the oceans of data that are generated from a seemingly infinite number of sources, and translate this knowledge into deploying smarter drivers and safer fleets.

With great knowledge comes great responsibility. However, there is more driver information and data than we could possibly begin to manually distill and make useful. In the face of infinite data, the “duty to act” becomes a daunting, and in many cases, almost insurmountable task. So I start by asking, what are the needed steps to manage and act on the data we collect from so many disparate sources?

I hear about many interesting — and concerted — efforts by motor carriers to develop comprehensive, data-driven Driver Scorecards, so I thought I’d write up a summary of the steps required to reach a successful, and data-driven, outcome on that journey.

Step 1: Identify Relevant Safety Data

Given the oceans of data available, it can be difficult to identify and prioritize the sources of data that are relevant to driver safety. Some sources to consider in the equation:

CSA/MVR/Pre-hire/DQ Files/ELD Mandate/Telematics/In-cab Video/Driver Training/IOT—Active Safety Systems


Step 2: Acquire relevant data from multiple silos 

Now, this can be easier said than done. Of course, data flows off of trucks and into your company reports, but there are other means, like APIs, to acquire the data you need for the full picture. Two skill sets are critical at this step: a safety expert who understands the value of identifying safety indicators, and a skilled IT expert—someone who can navigate the technology of multiple disparate, disconnected platforms from multiple suppliers including the government, and then bring it all together into a meaningful report.



Step 3: Analyze the now combined data to understand the who, what, when, where, why and how of driver behavior and risk.

This final step requires statistical analysis, or what is now popularly called data science. After the data is wrangled, we must then convert it to identify and communicate risky behavior. We make this data meaningful and actionable by pulling together the information that a safety director needs to deploy the resources, training, rewards, or discipline to impact driver behavior and reach the goal of safer fleets and safer roads.

In my travels across the country visiting valued customers and attending industry events, I see and hear a lot about the great work being done by carriers working hard to tackle this 2018 challenge—the data-driven Driver Scorecard. Here at Vigillo we have spent the better part of ten years tackling the challenges of the data deluge and turning it into actionable intelligence.

If you want to learn more about how we have mastered these steps in designing a data-driven Driver Scorecard that automatically pulls all of your disparate driver data together and makes it intelligent and actionable, I invite you to personally get in touch. I would love to hear about your data challenges and share with you what we have learned.