Drowning in data but starved for insights? It's a common problem in the field of workplace safety where having access to a vast amount of injury data is both a blessing and a curse. While this data is a valuable resource, it comes with its own risks, particularly when we lack the proper tools and strategies to effectively analyze and use it.
But there’s hope! By implementing the principles of data rigor, working with leading and lagging indicators, and technology, we can maximize the value of our injury prevention. This enables us to turn the overwhelming amount of data into actionable insights that can help prevent future accidents and injuries.
The principles of data rigor in decision-making
Data rigor is essential for collecting and analyzing high-quality and reliable data to inform decision-making and identify workplace injury risks. To achieve this, it's important to:
- Set clear goals and objectives
- Consult with stakeholders
- Use appropriate tools to analyze data
- Identify trends, patterns, and insights
- Continuously collect and analyze data
- Regularly review and clean data to ensure accuracy
- Implement data visualization
- Prioritize relevant data and collaborate with subject matter experts
- Invest in data analysis training and techniques
Balancing leading and lagging indicators for better insights
Perhaps the most important tool to uncover the value in injury data is utilizing both leading and lagging indicators. Leading indicators are used to detect hazards to prevent injuries before they happen, while lagging indicators analyze past incidents to uncover clues to their root causes.
Examples of leading indicators include safety engagement discussions with workers, safety-related communication, workplace safety inspections, hazard identification, and interventions to prevent injuries.
Examples of lagging indicators include the rate and severity of injuries, the number of lost workdays, the rate and cost of workers' compensation claims, and the ongoing number of hazardous movements per hour.
How technology can help
Technology can be an invaluable tool for collecting data and monitoring performance against leading and lagging indicators.
For example, in jobs prone to musculoskeletal injuries, we can use wearable movement sensors and AI vision processing technology to measure leading indicators. Wearables provide key data points like hazards by job roles, departments and tasks, and identify workers in need of additional coaching. AI vision processing technology can be used to conduct task risk assessments to identify the body mechanics involved and facilitate interventions.
The resulting insights promote action through encouraging safety engagement, identifying coaching needs, facilitating efficient inspections and interventions, and mitigating risks. Collecting data on these activities helps measure performance against leading indicators.
To measure the effectiveness of a safety program, we need to work with lagging indicators as a baseline. For example, an increase in musculoskeletal injury rates may indicate the need for implementing technology such as wearables and vision processing. By tracking lagging indicators such as injury rates, lost workdays, and claims, we can measure the success of the interventions and technology.
Wearables can collect data on the number of hazardous movements per hour, which is a useful lagging indicator. A sustained reduction in hazardous movements indicates a successful safety program.
By enlisting the help of both leading and lagging indicators, we can build a holistic approach to workplace safety that safeguards the well-being of all.
From data to action
With all the data that can be collected and analyzed, it's easy to become overwhelmed and lost in the sea of information. But simply collecting and analyzing data isn't enough to improve workplace safety. The real value of data lies in using it to inform action.
The hierarchy of controls provides a structured approach to convert data insights into effective actions that reduce the risk of injuries. By using the insights obtained from our data we can work through each level of the hierarchy of controls, ensuring that actions are targeted, effective, and data-driven.
Advancing data analytics: Moving towards sophistication
To effectively turn data into action, it's important to understand the different stages of analytics sophistication. The PwC's model describes four stages: descriptive, diagnostic, predictive, and prescriptive.
Beginning with basic reporting on safety incidents, we can advance to using diagnostic analytics to merge multiple datasets and identify correlations between worker behavior and workplace accidents. Predictive analytics can be used to understand factors that contribute to incidents, and ultimately, optimization of analytics can help make data-driven decisions to optimize safety functions.
By assessing the stage of analytics sophistication maturity, we can identify opportunities to grow and improve in gathering actionable data to prevent injuries and reduce costs.
Key takeaways for successful data analysis
Looking towards the future, effective data analysis is crucial in preventing injuries, reducing costs, and retaining staff in the current labor shortage crisis. By establishing data rigor, working with leading and lagging indicators, utilizing wearable technology and AI, and working through the hierarchy of controls, we can gather rich, actionable data and turn insights into actions.
As data analytics continue to evolve and mature, we must strive to move towards predictive and prescriptive analytics to optimize safety functions and make data-driven decisions. With a carefully considered plan, we can stay ahead of the curve and ensure a safe and productive workplace for all.
