The Shell company now uses predictive data analytics to assess potential workplace injuries on oil rigs worldwide, according to “Dr. Data Video: Five Ways Your Safety Depends on Machine Learning” by to analytics expert Eric Siegel on PredictiveAnalyticsWorld.com. Shell’s computers can weigh measurable factors, such as employee engagement in safety procedures, and predict when and where injuries are likely to occur.
Siegel also highlights Accident Fund Insurance, which developed a list of medical conditions that result in higher cost occupational injuries. Obesity and diabetes, for example, can make injuries worse and recovery times longer. With that information, companies can use predictive analytics and implement preventive measures aimed at avoiding injuries (and costs) as much as possible.
The business of preventing workplace injuries has become a precision task in recent years, thanks to data analytics. Graduates with a bachelor’s degree in occupational health and safety can expect that many of their tasks and most of their decisions will be data-backed in some way. They will be entering a field with a broader range of occupational health and safety career opportunities, thanks to Big Data.
What Types of Data Figure into Workplace Injury Analytics?
The routine nature of an average workday makes the occupational health and safety industry a perfect candidate for the adoption of data analytics software and data-driven solutions. Whether we consider a factory worker regularly exposed to dangerous chemicals, an office worker in a fast-paced position where stress and anxiety are common, or a construction worker operating dangerous heavy machinery, the right tools and software can yield valuable data that safety personnel can use to improve workplace safety conditions.
To maximize the effect of predictive analytics on workplace safety, analysts need to use the correct datasets. Only data that pertains specifically to safety should be collected for inclusion in the analytics algorithm. Superfluous information can alter analysis. In HVACR Business’s “Unlock Safety through Data Analytics,” Ali Vahed lists these usable data categories:
- Description of reported safety incidents
- Recordable incident rate
- Injury costs
- Safety training and certification compliance
- Office safety incidents
- Fleet accidents with details
- Tools in use
- Design elements
- Workers compensation details
- Severity details
- Levels of experience
The information gleaned from the dataset analysis can help safety workers, managers, and inspectors build a better predictive model of which workers are likely to experience which types of injuries; when those injuries are likely to take place; on which equipment; using which tools; and how expensive the injuries might be to a company over time.
On the flip side of worker safety, legally speaking, is the inevitable litigation that takes place after an incident. Analytics from the claims side of occupational health and safety also leads to the implementation of protocols and procedures that improve workers’ safety in the future.
“Experts say data such as injured-worker demographics, injury data, timelines of claims and information on claims that end up in litigation can all help claims managers guide future outcomes,” Louise Esola explains in the BusinessInsurance.com article, “Predictive Analytics Emerges as Workers Comp Best Practice.”
Workplace injuries provide the impetus for stakeholders to work for safety improvements. From CEOs commissioning a new safety program to factory workers adopting commonsense safety procedures, the goal is the same: to reduce injury-related losses to both human and financial capital.
The Present and Future of Workplace Safety Predictive Analytics
From about 2014 to 2018, Toronto-based Deloitte Canada illustrated just how useful predictive analytics could be in preventing workplace injuries. In Cos-mag.com’s “New Data Sources Leading to Innovative Uses for Predictive Analytics,” occupational health and safety writer Linda Johnson explains the decision to incorporate predictive analytics software into the company’s safety program.
Over the span of just three years, Deloitte Canada’s employees went from identifying a few hundred hazards per year to identifying between 10,000 to 15,000. In the article, Andrew McHardy, senior manager at Deloitte, says that his team started looking into company systems “that contain relevant data points, such as training, operations, maintenance, overtime, shift schedules and nature of the task.”
McHardy also points out that valuable information may come from third-party sources. Further incorporation of third-party data can improve the accuracy of predictive analytics in the workplace safety field.
According to marketing manager Jessica Shields in “Predictive Analytics: A Powerful New Tool” on ISHN.com, some of the datasets likely to be incorporated into predicative analytics in the future include:
- Human capital management data such as workforce management and scheduling
- Peer data
- Third-party data such as weather information
- Data from other connected devices such as badging systems, telemetry solutions, and fatigue monitors)
Everything from prioritizing inspections to planning equipment maintenance demands reliable, detailed data and data-savvy professionals to apply it. And as the data industry matures, the ability of predictive analytics to inform actionable, useful models will also improve. Those entering the occupational health and safety field have an opportunity to make data work for them, making their jobs more effective and, ultimately, workplace conditions much safer for workers.
Eastern Kentucky University’s Bachelor of Science in Occupational Safety Program
Eastern Kentucky University’s online bachelor’s degree in occupational health and safety program is designed to teach students how to identify and analyze potential workplace hazards, infractions, and risks.
Experienced safety professionals guide students through environmental health and safety classes online, covering modern trends in employee engagement and the establishment of a safety culture in the workplace. For more information, contact EKU today.
Recommended Reading:
4 Ways Big Data is Revolutionizing Emergency Management
Mobile Apps Designed to Enhance Occupational Safety
5 Safety Training Resources for Occupational Safety Specialists
Sources:
Five Ways Your Safety Depends on Machin Learning – PredictiveAnalyticsWorld.com
Unlock Safety through Data Analytics – HVACR Business
Predictive Analytics and Workers Comp – Business Insurance
New Data Sources Lead to Innovative Uses – Cos-mag.com
Predictive Analytics: A Powerful New Tool – ISHN.com