Technology has been helping many times, but the impact of cutting-edge technology that saves lives cannot be overstated! Data analytics has been at the forefront of saving lives, from using technology to advance the medical sciences to addressing the ongoing pandemic situation. Thus, it should come as no surprise to anyone that one of the most prominent uses of data analytics and visualization platforms has been to reduce workplace injuries and fatalities.
The impact of reducing workplace safety incidents is enormous. When a company makes a conscious effort to minimize the safety-related incidents at the workplace, it results in lesser time-off taken by employees, reduction in compensation payments, and an overall healthier workplace environment. Unfortunately, while many companies and business stakeholders want to prioritize workplace safety, they often lack the technology backing that will help them achieve the best results. Today, it is imperative that workplace health and safety management software comes with data analytics capabilities that empower users to implement a data-driven approach to eliminating safety incidents at the workplace. In this blog, I wanted to highlight the importance of predictive analytics and its crucial role in workplace safety.
What is the importance of Data Analytics in Workplace Safety?
Data analytics is the gateway to predictive analytics that can help spot patterns and predict future trends. Some of the best data analytics and visualization platforms can unearth actionable insights from massive data sets. Today, leveraging this technology to address workplace safety is a growing trend. At workplaces such as construction, manufacturing units, the workers are constantly in contact with heavy machinery and can be prone to human errors leading to severe injuries and fatalities.
A. Predicting Common Injuries
The power of predictive analytics lies in spotting common patterns that bring forward the anomalies in the current measures taken to avoid workplace injuries and fatalities. Leveraging the power of data analytics, stakeholders of the business can import data from various sources to understand the common causes for most workplace safety incidents. Organizations that have adopted data visualization platforms can thus swiftly create visual reports showcasing the injury patterns and address them to predict and reduce the injuries accurately. When it comes to addressing the significant reasons for workplace injuries, it is all about connecting the dots and finding the hidden culprits. Predictive analytics can help many businesses anticipate the damage and take measures to prevent them, leading to lower workplace safety incidents.
B. Analyze Data From Multiple Locations
Many of the health & safety management systems today come with data analytics in-built in the solution. However, in large-scale businesses, different factory and manufacturing unit locations can have other challenges. When it comes to safety measures, it may become difficult for the business stakeholders to devise a uniform strategy. Thus, analyzing data from multiple locations enables the safety managers to accurately understand the stop-gaps and configure the procedures to match the specific safety needs of the site.
C. Real-time Insights
Many businesses already collect massive data from various sources such as business apps, wearables, manual log sheets, etc. But, not all business stakeholders understand how to gain actionable insights from the data to address anomalies in business processes. A robust data analytics tool power of data-driven decision-making and real-time insights, thereby enabling users to address workplace safety concerns by studying the data points in real-time.
Addressing safety measures by leveraging data analytics has plenty of advantages. Apart from saving lives, reducing the cost incurred by the business to compensate the injured workers also impacts the company’s revenue. With the advancement in workplace health and safety management software, there will be a time when a majority of the business will fully adopt data analytics.