Originally published in Fleet Management Weekly 11/4/2020
By Jim Noble, Senior Vice President of Risk Engineering, eDriving
How do you effectively use aggregated driver risk insights to reduce your exposure to crashes/incidents/injuries and possible business-ending events?
Driver risk management is a complex picture of dozens of interconnected influences that, layered together, create a total picture of risk. Using just one element of that picture, even an important element like today’s in-vehicle intelligence provided by telematics or cameras, is like viewing the old console tube-type television picture. It just doesn’t provide the high definition picture you get with the TVs of today which can process massive amount of data to give that “I can see the individual grains of sand” picture clarity.
For this reason, I’m always amused by safety service providers that can “reduce your risk by 20%” based on one element of a driver safety program. One element alone may give you a small part of the picture, or a less detailed view of the overall picture. If you want a high definition overview, you need the “bigger picture”. Developing a risk portrait of a driver, an organization, or even a portfolio of businesses requires the ability to incorporate massive amount of data from multiple variables, sometimes in different ways or combinations.
Over the past few years, we have learned a great deal about the interconnectivity of driver risk. In addition, OHSA, HSE, and EH&S guidance points out that the root cause of a crash does not happen in a vacuum. Instead, there are several casual factors along the way that trigger the performance failure which results in a crash/incident, near-miss or on-road violation. Attempting to reduce or eliminate these casual factors requires aggregating and monitoring more than one input from more than one source. But it’s not as simple as bringing all of the data into one place. The aggregated data must also be normalized, categorized by relativity to risk, analyzed and presented as risk insights that are easy to understand and act on. Only then can you obtain the high definition picture you’re looking for.
Single source providers are not able to give you this clarity into risk factors. There is a common argument about leading and lagging indicators. Some say leading indicators are all you need. Some still insist they can make good risk management decisions by looking in the rear-view mirror. In fact, both leading and lagging indicators are important. A smartphone app is an excellent tool to measure driver behavior and give you a high fidelity driver score, but it cannot tell you if the driver has a disqualifying traffic offense such as a “driving under the influence” conviction. An in-vehicle camera is an excellent device for giving an accurate picture of what caused an event, but it will not tell you the driver’s hazard recognition skills or past driver training history. Motor Vehicle Records/License Checks can contribute to a predictive model of future risk, but relying on traffic violations alone could result in at-risk behavior going undetected between violations.
So, how do you effectively use aggregated driver risk insights to reduce your exposure to crashes/incidents/injuries and possible business-ending events? First, everyone in the organization has to recognize the importance of these insights and how you are going to use them to reduce risk in your business. Frontline managers must be educated in the use of, and invested in the outcome of, the actions these insights create. Second, you must broaden your viewpoint of what the insights tell you. A “gotcha” program for drivers is not effective. However, using the full set of data available to you – including collisions, incidents, license violations and behavior-based telematics insights – to look for operational risk or organizational risk will go a long way to effectively reducing the risks your organization faces when driving for work purposes.
This can be accomplished by looking at all pieces of the risk landscape and striving to obtain the “big picture”.
About Jim Noble
Jim has been associated with eDriving for almost 20 years as a customer, a consultant and as a team member. His 40+ years in transportation encompass leadership positions in fleet operations management, logistics management, advocacy, driver safety and global risk management. As lead Risk Engineer for eDriving he works to find innovative ways of harnessing the power of eDriving’s “Big Data” to produce actionable and easy-to-understand insights aimed at reducing customers’ risk profiles.