Safety Articles

Using Big Data to Keep Workers Safe

Photo courtesy of Predictive Solutions Corp.

Lately, safety practitioners seem to be on a quest similar to that of King Arthur and his Knights of the Round Table. But instead of trying to find the Holy Grail, safety professionals are trying to find something similarly elusive: a single, all-encompassing, leading indicator upon which they can manage their entire safety function.

Well, I have good and bad news on this topic. First the bad news: A single, all-encompassing safety leading indicator is like the Fountain of Youth — it probably does not exist. However, there is some good news: There is a single leading indicator that seems to stand above all the rest with regard to its ability to explain and predict workplace injuries.

This leading indicator is the information that is derived from conducting safety inspections and observations. For instance, a research study conducted in partnership with a team from Carnegie Mellon University (CMU) found that 75 percent of the variation in the frequency of safety incidents can be explained by the information derived from safety inspections and observations.

Further, this team was able to build a computer model that could predict future incidents with accuracy rates ranging as high as 80–97 percent. How’d they do this? You guessed it — using safety inspection and observation data.

So, is your organization using this powerful leading indicator that can explain 75 percent of incidents and even predict them as much as 97 percent of the time? If not, why not?

How Can Safety Inspection Data Be So Powerful?

Safety inspections and observations are powerful because they are both a direct and indirect view into the myriad factors that affect workplace safety — things like training, process controls, and even culture.

If inspections and observations are done correctly, they can directly measure things like adherence to proper process controls. For instance, they can determine if employees are actually following safety procedures for things such as lock-out-tag-out processes, fall protection, and proper machine guarding.

Further, inspections and observations can also indirectly measure other risk drivers such as training and culture. For instance, if during an inspection an employee is found not following the proper fall protection procedures, a quick conversation with that employee can determine why. The employee might respond by saying, “I had no idea we had such procedures…this is my first week on the job!” This would probably suggest a new-hire training issue that needs to be addressed. Alternately, if the employee responds, “I’ve been doing this work this way for 20 years and have never been hurt,” then there is probably a cultural issue that needs to be addressed.

Regardless, when done properly, observations of the conditions on the worksite, and the behaviors of employees within those conditions, can provide information about both the direct and indirect causes of future safety incidents. The CMU team’s analysis proved this, and savvy safety professionals have intuitively believed this for decades.

Like any system analysis, it is important to review the inputs of the process in order to understand, and even predict, the outputs. Similarly, safety observations and inspections give us insight into the inputs so that we can explain, and even predict, the outputs. In this case the outputs are occurrences of safety incidents — or better yet, the lack-thereof.

How Can This Be Applied in the Real World?

While helpful, it’s not necessary to have a team of world-class data scientists and machine-learning experts at your disposal to make use of inspection and observation data. Much of the team’s work has simply confirmed what many safety folks have been professing for years: safety inspections and observations are a critical component to operating a world-class safety function.

In fact, just the process of systematically and regularly conducting safety inspections and observations can yield better safety outcomes, especially if employees are engaged during the process by discussing the safe and unsafe observations that are found. The research found that the safest jobsites often involved non-safety folks in the inspection process. If you can involve operations employees in your process, you can instill a focus on safety that goes outside just the safety functional group.

If your safety data set is not too large, basic data analytics can be used to answer basic business questions using standard software systems such as Microsoft Excel. However, if your data set is large, or if you are trying to answer more strategic business questions such as “where and when will my next safety incident occur?” more advanced analytics systems may be required.

The graphic shows the different types of analyses that can be conducted against safety data sets. As you move up the pyramid and address more strategic business questions, more robust analytics techniques and systems are usually required.

Preventing Incidents

Legend has it that King Arthur never found the Holy Grail and that his quest was futile. However, you can find and use one of the most powerful safety leading indicators available today by simply conducting safety inspections and observations, and then analyzing the data that comes from that process. As you move up the analytics pyramid, you will not only be able to explain what is causing safety incidents, but you can also start to predict them. Once safety incidents can be predicted, they can be prevented, and we can all realize our safety quest of a zero-incident workplace.

About the Author:

Griffin Schultz is the General Manager of Predictive Solutions Corporation, a fully owned subsidiary of Industrial Scientific Corporation. Predictive Solutions’ vision is to end death on the job, in this century. Its strategy to achieve that vision is to predict workplace injuries so their customers can prevent them. Griffin earned his MBA from The Wharton School at The University of Pennsylvania and an undergraduate degree in History and Economics from Wittenberg University. For more information, contact: Predictive Solutions Corporation,

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