The Era of Big Data Analytics in Safety

By Griffin Schultz
General Manager
Predictive Solutions Corporation

One of the hottest topics in workplace safety in 2013 has been the use of safety data analytics. Companies are starting to effectively analyze the data they’ve been collecting over the last many years, and as a result, are gaining insight into how to improve their safety processes. 

Why is the topic of safety data analytics so hot at the moment? There are several reasons; some are global in nature, while some are specific to safety.

First, we are in a global era of "big data." A Google search of the phrase "big data" returns 1.85 billion results. IBM has estimated that 2.5 quintillion bytes of data (that’s a 25 with 18 zeros after it) are created daily, and that 90 percent of the world’s data has been created in the last two years alone. Big data sets in safety are no different. A top 10 global general contractor is collecting 150,000 safety observations each month, meaning it will collect nearly 2 million observations in just one year. Multiply that amount across its hundreds of projects around the world, and we get to some staggeringly high data levels very quickly. 

In addition, data storage capabilities and computing power are expanding. The smartphone that is in your pocket is as powerful as desktop computers of just 10 years ago. This increased computing power has resulted in vastly improved data analytics capabilities across the world’s big data sets. Further, these powerful computers have supported great advancements in the field of machine learning where computers learn without being explicitly programmed. Machine learning systems devour the world’s big data sets in order to identify trends and patterns in the data that allow computers to predict future outcomes – exactly what we are trying to achieve through the use of leading indicators. 

Specifically within safety, many companies are turning to data analytics because they’ve wrung all the value they can out of more traditional safety strategies like root-cause incident analysis, safety culture improvement, training, and general safety consulting. While world-class companies employing these traditional strategies have seen dramatic reductions in their incident rates, they are having a hard time getting to that "last mile," or a zero-incident rate, and are turning to data analytics for help. 

Business leaders are starting to expect safety functions to analyze their data as rigorously as other functions within their business. They know from their experience in other functions, that if they have data, they should be able to analyze it to gain key insights.

According to Tom Davenport, in a book titled Competing on Analytics, it depends on what level of data analytics one is employing. Figure I below is adapted from Davenport’s book, in which he says that in order to address more compelling business questions (the right-hand side of Figure I) we need to move up the analytics pyramid and employ more advanced and even predictive analytics (the left-hand side of Figure I) on our big data sets. 

Figure 1: What Can Be Done with Data

If all a company is doing is collecting data and then performing basic data access and reporting on that data (activities like standard reporting, queries and data drill-downs at the bottom of the pyramid in Figure I), then the questions that can be answered will be very basic. Users will be able to tell what happened, as well as where, when, and how often, but that’s about it.

In order to create leading indicators that can answer more strategic business questions like "Why is this happening?" and "What if these trends continue?" we must move up the analytics pyramid by employing advanced analytics techniques such as statistical analysis and forecasting and extrapolation. It is only then that we can create the forward-looking leading indicators that our leaders outside of safety really want, and are starting to expect.

Davenport suggests that to answer the penultimate business question of "What will happen next?" we need to go beyond even advanced analytics and employ predictive modeling. Predictive modeling has proven to be extremely successful in returning value in other business areas like sales forecasting, customer retention, and customer upselling. And over the last few years, predictive models have been developed in safety to predict future workplace injuries so that they can be prevented. 

A research team at Carnegie Mellon University (CMU) in Pittsburgh, Penn. – part of the same group that helped IBM build the Deep Blue and Watson supercomputers – used four years of real-world safety data to build computer predictive models in safety. These models were tested at overall accuracy of rates of 80 to 97 percent. With an r-squared correlation measure of .75, the research team was able to explain 75 percent of the variation in a company’s injury rate based on their safety inspection and observation data.

Talk about a leading indicator! This model, which is now in production and being used by nearly 100 companies, uses a company’s safety inspection and observation data from the last three months to predict the number and location of safety incidents over the next 30 days.

A Real-World Safety Prediction Story
On Feb. 27, 2013, a worker at a location in Hazard, Ken. lacerated his arm with a box-cutter and had to go to the hospital for stitches. The employee was not significantly hurt, but nonetheless the company recorded its first lost-time incident at that location in nine years. Because this location was the safest within this business unit, there were no obvious indicators to suggest heightened risk of injury at this plant.

However, on Feb. 1, the CMU-developed "Red Flag Prediction Model" identified this location as being at high risk of having increased safety incidents. The machine-learning computer model used the last three months of safety inspection and observation data from the Hazard location to predict that it was going to have an incident – when no other indicators were suggesting anything of the sort. The computer model saw trends and nuances in the safety inspection and observation data that allowed it to derive conclusions that were beyond the reach of traditional safety measures.

Now, keep in mind, the worker in this prediction success story still got hurt. While predicting injuries is getting easier, preventing them is as hard as it’s ever been. The Red Flag Prediction Model is still quite general in that it cannot inform a company of exactly who, when, or how someone will get hurt. But over time, as more granular data gets collected and analyzed, it is believed that these prediction models will only get more specific and accurate.

Regardless, this same company has had six locations "red flagged" by the prediction model over the last 12 months. Four of these six sites had incidents. The company is unsure if the two that did NOT have incidents were due to their active intervention to prevent injuries, or because the model simply made an incorrect prediction. Regardless, with four out of six site predictions proving accurate, this company now has a high confidence level in the predictive model and has made it a key part of their safety program. When a location is red flagged, they have a safety stand-down at that location and review their highest areas of risk. They use the model like the "check-engine" indicator in a car. It can’t tell them exactly what is going on, but it can point them in the direction of their highest risk areas.

The Top of the Pyramid
As Davenport suggests, once a company can predict what will happen in the future, they can optimize their response to this prediction and achieve the best outcome (the pinnacle of the analytics pyramid in Figure I). In safety, that means predicting and then preventing workplace injuries.

The company who had the box-cutting incident already had a well-below average incident rate for their industry. They had achieved this by employing many of the traditional safety strategies discussed earlier in this article. However, to get to the "last mile," or zero incidents, they have turned to new strategies, including advanced and predictive analytics on their safety big data set. Employing technology, they have increased their safety inspection data set by 700 percent which has fueled the advanced and predictive analytics resulting in reductions across key loss metrics. They reduced their recordable incident rate by 76 percent, their lost-time incident rate by 88 percent, and their lost-work day rate by 97 percent. By employing data analytics strategies up and down the pyramid in Figure I, they estimate their return on investment from their analytics efforts at around 20,213 percent from loss category reductions alone. No wonder safety data analytics is such a hot topic!

 Griffin Schultz is the general manager of Predictive Solutions Corporation, whose vision is to end death on the job, in this century. Schultz has an MBA from the Wharton School and has deep experience using technologies to solve difficult business challenges. He can be reached at gschultz@predictivesolutions.com.