Humans are cognitively wired for pattern seeking. Unfortunately, these “patterns” are laden with our judgments, experiences, biases, and expectations, such that we struggle to differentiate between real patterns and random events. This bias is even more prevalent when we are fed information one instance at a time (as in the case of workplace incidents).
To ensure we are identifying true patterns and effectively using safety data for prediction requires overcoming two common tendencies: (a) focusing on single incidents in isolation and (b) analyzing only the incident data, independent of other sources of data in the organization. Many users of safety data have not considered connecting the dots beyond injury reports or studying many instances all at once. Sometimes, there are just too many organizational barriers in the way or the resources are unavailable.
For most safety professionals, incident investigations are the primary resource for diagnosing problems and eliminating exposure. However, investigations provide information limited by the occurrence of an incident, with more severe incidents providing more information. This practice leaves most safety professionals looking at the outcomes and extrapolating factors from there. It is like explaining how rotten apples fell out of a tree by only looking at the rotten apples, and ignoring the tree. This practice creates distortion and focuses more attention on immediate causes (e.g., the employee put his hands in the equipment), rather than the larger contributing causes like equipment speed, on-the-job experience, employee fatigue, or overtime. Furthermore, the population who did not get hurt never enters the equation. For example, given only records of injured employees, one may conclude that newer employees appear to have more incidents. However, if the entire population is made up of newer employees, there may be no significance to that fact.
In our experience helping clients analyze their own data for precursors and other important factors, we have identified the following best practices that can make your safety data yield more fruitful insight:
In the end, finding patterns in the data helps us eliminate risk exposure. When we find out what the incident precursors and related factors are, we need to eliminate them or modify the exposures so they are no longer precursors or factors of risk. Then we will need to start the learning process all over—perhaps with new measures of safety, since we will have no incidents left to predict.