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How to Evolve Safety from Protection to Prevention to Prediction

How to Evolve Safety from Protection to Prevention to Prediction

Tomorrow’s self-driving cars provide us with a glimpse of the potential impact big data can have on safety.

I’m in the market to buy a new car. My shopping experience thus far has led me to be absolutely amazed by how much has changed in the automobile landscape in just a few years. The vehicle I’m replacing is only six years old, but in terms of technology and capability, it already feels like an antique.

One area in particular that stands out is the amazing pace of evolution of car safety. Automobile safety has gone from protection, to preventions, to prediction in what feels like a matter of years.

The emergence of automobile safety initiatives in the 1960s and 1970s – driven by the research of people like Ralph Nader – led car manufacturers to include features such as seatbelts as standard (and legally required) equipment. This soon evolved into air bag technologies and structural engineering to account for crumple zones, which transformed vehicles which were once considered “unsafe at any speed” to vehicles that had built-in protective equipment and capabilities. Today, it’s estimated that seatbelts alone save up to 15,000 lives per year in the United States.

Automotive Safety Evolves

As the next phase of safety emerged, cars began to leverage advances in technology to compliment those initial protection capabilities with prevention technologies. These days, it’s not just seatbelts and airbags protecting drivers and passengers.

As you walk through a car showroom, you’d be hard-pressed to find even a basic model that lacks a traction control system or anti-lock brakes. These two technologies are designed to augment a driver’s skills by preventing skids and wheel lock up, which can lead to loss of control and ultimately accidents.

As the computing power underlying those preventative technologies has become cheaper and more powerful, car safety again has evolved from preventative to predictive capabilities. As I browse my local car showrooms, I’m struck by the safety features that now are considered “standard” in many vehicles: lane departure systems, adaptive cruise control and collision detection, to name a few. All of these are designed to not only enhance my driving skill, but also take over certain tasks for me. If a driver’s focus drifts or a distraction sets in, these systems go far beyond simple warning systems and include intelligent steering and braking assist and predicting and avoid hazards before they occur.

The secret behind these featured is the vast amount of data being collected on an ongoing basis in a modern vehicle. Today’s cars are as much moving computers as they are transportation, and have systems and models crunching data at an incredible rate to use in real time. As this evolution leads us to the promise of self-driving cars, it’s estimated that tomorrow’s cars will create over 1GB of data per second.

From Cars to Workplace Safety

Tomorrow’s self-driving cars provide us with a glimpse of the potential impact big data can have on safety. And that potential isn’t restricted to cars and consumers: The modern workplace also is seeing a similar evolution in safety – from protection, to prevention, to prediction.

The first phase of workplace safety, much like the emergence of automobile safety, was focused on protection. In this case, personal protective equipment (PPE). PPE such as hardhats, gloves and boots allowed workers to engage in potentially dangerous jobs by offering physical protection from hazards.

As enterprise software tools became more pervasive in the workplace and as regulatory pressures from organizations, such as OSHA increased, safety programs evolved from focusing on protecting the worker’s body during exposures to hazards to demanding far more robust reporting and data capture requirements to predict and prevent exposures.

Occupational health and safety in the workplace no longer is about managing PPE programs. It now includes tracking not only lagging metrics such as injuries and lost time, but leading indicators such as job safety analysis, near misses and employee engagement surveys. The objective of this data collection is to prevent workplace safety and health incidents from occurring.

Once again the underlying secret behind these programs (much like in today’s modern automobiles) is a vast amount of data being collected and managed.

Along with all this data collection comes a lot of opportunity. That data allows us to take the world of health and safety programs from today’s preventative focus, to the promise of tomorrow’s predictive one.

Our early modeling has unlocked some tantalizing initial nuggets of data-based predictive findings. Like self-driving cars, these insights can allow an EHS professional to not only identify hazards but actively (and specifically) prescribe what to do about them.

One example is the discrepancy and variance that exists within an organization. Within large organizations, we have seen that from a safety standpoint, there can be as much as a 14 times variance in safety performance between top-performing locations and facilities and bottom-performing ones. By modeling out the “secret recipe” of those top performers, organizations can close the gap between their best-performing facilities and their worst. The prospect of having this safety performance equalized can lead to a significant overall improvement in company performance. These are results that can have a tangible impact on an organizations bottom line.

Having a large pool of data allows this. Data-driven models can show us in a quantitative way the impact that factors such as employee tenure and location size have on not just safety metrics, but on things like injury tracking and the tracking of leading indicators.

Data has put the world safety on the path towards a predictive and prescriptive future. As the automobile industry has foreshadowed, we are on the cusp of another golden age in safety as it relates to the workplace. We’ll soon be able to look back and see this evolution from protection, to prevention, to prediction and be amazed at how quickly we’ve gotten here (and also wonder how we ever managed to do things before).

That’s what I think. What do you think?

(This article originally appeared on the Intelex Health and Safety Blog.)

Jason Dea is the director of product marketing at Intelex, where he works with the Intelex team to deliver their market leading occupational health and safety software solutions. He is an accomplished marketer with over 10 years’ experience helping develop the product roadmap and launching successful ROI-driven products to sustain business growth.

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