Canadian Occupational Safety

Feb/Mar 2014

Canadian Occupational Safety (COS) magazine is the premier workplace health and safety publication in Canada. We cover a wide range of topics ranging from office to heavy industry, and from general safety management to specific workplace hazards.

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By Chuck Pettinger, Ph.D. T he latest corporate buzzword is "Big Data." Thus, you cannot open a business journal without seeing the phrases big data, business intelligence and predictive analytics. Big data is often solely associated with managing large amounts of diverse data. But more accurately, big data is about asking new types questions, exploring hunches, and making data- driven decisions. If big data is not a part of your business world today, it will be in the very near future. And, there is no better place to start than within your safety department. The safety field collects a plethora of safety intelligence. Unfortunately, this critical data is often used too late, misused, or just plain ignored. Big Data, Culture & Near-Misses It is a competitive marketplace. Some organizations have quickly embraced big data and succeeded. Many organizations have been slowly shifting their focus from reacting to injuries to predicting them with the use of their safety "big data." To improve safety, organizations must first identify leading indicators of a strong safety culture. Since organizations cannot directly measure their safety cultures, leading indicators can be used as good proxies. As such, many safety professionals focus on "near- misses" as cultural proxies. Near-misses have been defined as unplanned behaviors or events that did not result in injury, illness, or damage, but had the potential to do so. However, some argue that near-misses happen after the fact and are nothing more than a less serious incident and a lagging indicator. Some would argue that near-misses are leading because they give us a glimpse into the future and could be predictive of an incident. Could both perspectives be correct? A Near-Miss Continuum In attempt to gather quality safety data, many companies have quickly instituted a near-miss program. Just as quickly, many employees have ignored their near-miss programs. In some cases, employees are confused as to what is considered "worthy" of reporting a near-miss. I would suggest a continuum that spans from the possible to the probable; from the lagging to the leading indicator. A "near-hit" could be considered more toward the "sharp end" of the continuum and closest to incident. For example, a superintendent could be walking his project and a wrench falls from scaffolding nearly hitting him. This near-hit falls in the "probable" realm and should be addressed immediately. It is a lagging indicator. The next level is similar, except no one, or nothing, was close to the wrench when it fell. This situation might be described as a "near-miss." This incident still has potential for injury and should also be addressed quickly. It falls closer to the lagging side of the continuum. The third level might be considered a "good catch". A "good catch" might be described as an instance where the superintendent discovers wrenches laying on scaffolding without the required toeboards installed. Although no incident occurred, this good catch has the potential to cause harm and thus should be assessed. Finally, the least severe could be called "error-likely". For example, a safety professional notices sub- contractors erecting scaffolding for a small job. Because of the short-term nature, it is (error) likely that these sub-contractors might not install the required toeboards. This leading indicator might motivate the safety supervisor to attend the sub-contractor's pre-job brief to ensure toeboards are discussed and used. In regards to the question "are near-misses leading or lagging indicators?" the answer is yes! If we are proactive enough in our safety observations, "error-likely" and "good catches" can be considered leading indicators. When there are "near-hits" and "near-misses" where it is not a matter of IF something is going to happen, but WHEN, then those are lagging indicators. For Want of a Nail If we are to obtain quality near-misses, gather safety big data and begin to forecast where our next incident might occur, we need to enlist the masses in our search for leading and lagging indicators (i.e., crowdsourcing). Thus, if we look at near-misses as a continuum, we can not only asses the severity of these observations, but we can also gain the employee engagement needed to keep employees safer and ultimately help end death on the job this century. Chuck Pettinger, Ph.D., is a Process Change Leader at Predictive Solutions Corporation (www.predictivesolutions.com). He can be reached at cpetting@predictivesolutions.com. Big Data & Safety: Are Near-Misses Leading or Lagging Indicators? BroUgHT To yoU By

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