In any industry, the risk of workplace accidents presents a real, costly threat. From overexertion to slips and falls, companies seek to prevent common workplace accidents as much as possible. Unfortunately, no amount of preparation can completely remove the risk.
However, companies are beginning to implement better prevention strategies with the help of data. Information harnessed by our always-online devices provides the building blocks for safer workplaces. By using data analytics, businesses can better predict and prevent accidents.
Data enables many workplace solutions. Through the accumulation, analysis, and application of data into safety policies, you can continue to raise the number on your “days without accident” board.
Here’s how.
Accumulation
Using data begins with acquiring data. This can be a process undergone internally across the company or one that utilizes outside sources. With a vast number of potential sources for your data-gathering, you want to specify from the beginning what you want to serve as a sample group.
If you decide to use internal company data, there are a few valuable metrics you should gather. These are:
- Types of incidents
- Frequency of incidents
- Severity of injuries
- Events surrounding incidents
- Factors contributing to incidents
Any data-based approach to accident prevention should start with this information. Identify commonalities using corresponding numbers and incident types to start defining problematic areas.
If you are using data outside your company, there are several places you can look. Here are a few ideas:
- Government reports
- Health and safety studies
- Industry analytics
- Paid data services
- Email and social media surveys
An event like the coronavirus pandemic is a good example of when external data can be useful. Since it’s difficult to tell what health measures are effective through first-hand data in a short amount of time, broader data sources are necessary. Small businesses looking to protect their employees from an outbreak should examine reports from governmental agencies as well as the latest health studies regarding your industry.
Analysis
With enough data on hand, next comes the analytical process. Your workplace should invest in data tools that enable easy processing and updates to your database. This often comes in the form of cloud services and comprehensive dashboards.
Big data analytics help workers of any field understand their workplace metrics. In regards to accident prevention, data platforms can paint comprehensive pictures of where and why accidents are occurring.
If your data shows a correlation between workers on leave for a back injury and an uptick in overtime, you can safely deduce that these items may be linked.
Your data analytics should help you tell a story of how and why your workers are experiencing accidents. In narrative form, data is easier to communicate across a business. This will help you predict and apply all kinds of accident prevention measures.
Plenty of data analysis applications exist online and on mobile devices. These tools are available for free or through paid subscriptions with a wide variety of metrics available for you to track. Some of the most popular data analytics tools for businesses are:
- Google Analytics
- Tableau
- Excel
- Apache Spark
- R
Each of these platforms has different insights to provide when it comes to managing workplace safety. Find the best platform that works for your data, and begin applying workable solutions.
Application
The application of data can effectively reduce the rate of workplace accidents. Newly developed technologies like artificial intelligence and machine learning can help delve through data to predict problems. Additionally, an applied narrative surrounding workplace accidents can help businesses create a safer environment.
Predictive analysis is a key feature of AI tech. Big data can be compiled and scanned by an AI to make informed guesses as to when breakdowns and accidents are likely. This makes predictive maintenance possible. By keeping machinery and tools functioning smoothly, the risk to human workers is substantially decreased.
In few industries is this predictive application of data as essential as it is in trucking. Maintaining a fleet of vehicles means safer drivers. With the risks inherent in goods transportation, data applied to warn against harm are welcome and necessary.
In the future, the use of data will go so far as to make self-driving technology commonplace. Enough data allows predictive modeling so efficient that it is practically foolproof. With AI capable of precise, instantaneous decision making, workers and non-workers alike will be safer on the road.
Additionally, the use of smart devices on the Internet of Things (IoT) allows for greater tracking of information across work spaces. These devices can better analyze a space and alert employees to potential dangers like heavy loads, slippery surfaces, and extreme temperatures.
Informed, alerted employees and work spaces help businesses keep accidents to a minimum. For the safety of workers—and reduced liability costs—data is a massive benefit.
Final Thoughts
With the ability of data to inform the problem areas of virtually every business process, accidents can be prevented across industries. In driver safety, the use of that data is clear. In offices, warehouses, factories, and more, data can be applied to spot correlations that lead to accidents.
Perhaps the switch to a new cleaning agent has led to an increased number of slips on a factory floor. Without the data to show you this pattern, the problem might never be addressed. The power of data to keep employees safe and prevent workplace accidents is unlimited. Accumulate, analyze, and apply that data now.
