May 2022 Volume 4

EQUIPMENT & TECHNOLOGY

We Don’t KnowWhat We Don’t Know: Bridging the Data Gap With Technology By Paul Thurber

Safety has been defined through the years in many ways. For the sake of our common understanding in this article I will define it as, “The planned avoidance of loss.” This definition sets us up for a greater understanding of leading indicators and the predictability of incidents. We’ve all probably said or at least heard the phrase, “data is king.” Systems that collect data can change the way we’re currently doing things. I think we’d all agree that the availability of data before an incident occurs is better than our traditional reactive learning/response. The more information we have or know, the better we can protect and empower our team members. The lack of data has been a challenge for many safety programs over the past Without data, we find ourselves helpless in the prevention of specific and potentially recognizable unsafe behaviors, actions, or near misses. Recently, I attended the FIA Safety Conference in Detroit, MI. It was an awesome meeting to say the least –kudos to all who attended and participated. During the event, we had the opportunity to discuss the definition of a “near miss.” It was said that a near miss is nothing more or nothing less than “an unplanned event.” Think about it, when something happens by surprise or unexpectedly, there is an opportunity for incident and injury. After all, the only difference between a near miss (also commonly referred decade. But technology is bridging that gap. The Problemwith Lagging Indicators

to as a “near hit”) and an injury is a fraction of a second or an inch. Howmany times have we said or heard, “Whew, that was close!” Traditionally, data used for identifying leading indicators of an incident to injury comes from incident reviews and reactions to injuries. To this end, our learning only comes after the event and can only prevent the next incident if/when correctly addressed and communicated. This is why we commonly refer to this type of data as “lagging” because it can help to prevent the next accident or near miss but it does nothing for what has already occurred. In short, it is too late to prevent what has already happened. Another source of lagging data is teammate-driven and requires peer-to-peer observation and subsequent reporting. Hundreds, thousands, or perhaps even tens of thousands of observations go into our systems that sort and, if sophisticated enough, populate a spreadsheet or dashboard designed primarily to count and classify occurrences in hopes to identify a trend that can be corrected over time. But again, in most cases, this data is too late to predict and/or prevent the incident. The reactionary nature of lagging indicators makes them ill-equipped to proactively prevent injuries before they happen the first time.

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