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by Driving Dynamics

4 min read

Taking A Holistic View of Incidents to Achieve Maximum Driver Safety Improvements

Creative sign with the text - Big DataBig data is really here. The amount of details we can amass related to drivers’ activities behind-the-wheel is impressive and loaded with powerful, useful intelligence. However, organizations that have access to this may still face challenges achieving maximum results because a data-driven focus can have an unintentional consequence—To see the driver as a problem to be controlled rather than a resource to be leveraged.

This may lead an organization to view safety improvement as transactional instead of holistically. If we only focus on the premise that drivers are the cause of incidents because they do not follow the rules, then we’ll most likely ignore other equally important contributing factors such as processes, culture and leadership. The critical point to understand—safety improvement is inherently an organizational learning process and, to this end, the most beneficial learning method must be instituted. It’s not enough to have safe drivers. Using, in part, event-driven big data, successful fleet operators also engage in a continuous learning process to understand and address changing risk and organizational issues that may impede future safety performance. Because organizational safety improvements are driven by learning, let’s look at two different learning approaches.


Single-Loop Learning

Organizations with access to big data often gravitate toward the Single-Loop Learning method. It provides a quick, rudimentary conclusion to the perceived problem—often reactive in nature. This method takes a narrow view of incidents (e.g. using crash, MVR violation, telematics red flag data, etc.) and the attention is limited to immediate causes. The questions asked are:  Was our driver at faultWhat error occurred? Has this happened before to the driver? Do we apply punitive measures or remediation?

Here the objective is limited to identifying the immediate cause and applying a fix. For example, a policy requirement may close the loop by having the manager or an automated system notify the driver to be more careful. This messaging may include or require nominal training but it simply puts the driver on notice that his or her activities are being monitored and could have punitive ramifications. Take a step back now to see that the focus of this organizational learning method is predicated on: the driver is the problem.

Multiple-Loop Learning

Still making use of big data, Multiple-Loop Learning is a broader, proactive approach which supports safety improvements by helping an organization to learn how to stay safe. It still includes the questions found in the Single-Loop approach but also goes beyond the limiting view of the driver as the sole problem. Here the organization learns how it could have better supported the driver so that he or she could have been enabled to contribute to the organization’s safety goals. In other words, the approach steps back from the immediate situation in order to identify systemic causes. Still using data as the basis of its investigation and understanding, leaders will want to know: Is this something new or are we dealing with a frequency problem? Why is this occurring? Is there a reoccurring message delivered by our drivers related to these types of losses? Are their observations validIs there something we could organizationally change to support our drivers and improve results in this particular area? Are the remediation efforts previously taken effective? And, seldom asked but very important—From what we just learned, does this issue also apply to other aspects of our business which we can improve upon?

A very simple illustration: multiple crash incidents during the winter in the snow belt region of the country. In addition to a review of the loss notice summary for these events, what have the safety team or the managers learned from the drivers to acquire more detail and analysis other than “icy conditions”? Are there other factors to acknowledge that can be used to address changes in the way the organization operates?  From this Multiple-Loop learning process, perhaps the safety team is now in a position to provide valuable input to the fleet team on equipment selection (snow tires, all-wheel drive, etc.) for vehicles used in this region. Again stepping back and looking at this learning approach, the focus looks at the driver as a resource to be leveraged.

Change is often painful but to maximize an organization’s safety improvement potential, it must also become a great learning organization. The best way to achieve better performance is to exceed the quality and depth of how we learn from big data and put the following points into action:

  • View your drivers as a resource to be leveraged rather than the problem to be controlled.
  • Be proactive—make organizational learning routine. Don’t wait for a serious event to trigger learning. Continually respond and improve using fleet risk data instead of only limiting your focus to sequential incidents.
  • Put the focus on mining new knowledge at multiple levels—not to find fault.
  • Use this knowledge in manner that it is shared at all levels of the organization. Properly instituted, it creates momentum to do better across the organization and ultimately drives a culture of safety.

Remember, it’s not enough to only identify the immediate cause of incidents and apply a quick fix to get safer drivers. Organizations must look ahead and support safety improvements to learn how to stay safe by using event-driven big data and viewing the driver as the resource.


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Driving Dynamics

Written by Driving Dynamics

This article was developed by thought leaders and subject matters experts at Driving Dynamics.

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