You likely have a bunch of different spreadsheets, reports, and databases related to safety scattered across your organization. These data sources are compiled by various people in different departments. For example, HR may have information about driver certifications, while customer support keeps records of complaints about drivers and fleet operations holds maintenance records. You might not even know all the sources of possible data pertaining to accidents and injuries, of them, even if your job title contains the word “safety”.
Your organization’s safety record is probably pretty respectable. After all, accidents on the job are decreasing, as described in this news release from the Department of Labor. Check out this chart from the news release:
Still, there is no room for complacency. The National Safety Council cites nearly $200,000 in direct and indirect costs associated with a workplace injury that results in a doctor or hospital visit.
How do you apply those disparate and scattered sources of data to reducing the risk of accident and injury in your organization? There are three simple steps to using your data to improve workplace safety.
The first step is bringing together your using existing safety-related data using visual analytics. Just seeing your spreadsheets and databases correlated into intuitive visualizations that everyone can share delivers significant gains in safety. Creating a picture of your data clarifies relationship between data, puts data into a familiar context, and identifies potential problems in data quality.
For instance, the historical safety data for a trucking company may show which drivers were involved in accidents in the last five years. You could find that information easily by sorting a table of data. Placing that same tabular data on a map and a timeline tells you much more about exactly where and when those accidents occurred.
The next step is moving from viewing historical data to responding to today’s events by applying diagnostic analytics to alert users about current unsafe conditions. In the trucking example, adding alerts to a visualization of historical accident data can tell dispatchers when a driver is entering an area of frequent accidents. With that information, the dispatcher might caution the driver to reduce speed and drive carefully.
The third step in using data to improve workplace safety moves beyond the past and present to look into the future. Predictive and prescriptive analytics, based on your historical and real-time data, calculate the likelihood of future hazardous events and locations. The power to predict is the power to avoid or mitigate consequences.
Keeping with our trucking company scenario, predictive and prescriptive analytics may take the form of a prioritize list of tasks to perform to reduce risk and avoid accidents. For example, the dispatcher may change a driver’s rout to avoid areas with frequent accidents.
The journey from scattered spreadsheets to ubiquitous prescriptive analytics requires dedication, funding, and a vision of a safer workplace. Traveling the full journey may not make sense for every organization. Even so, stepping beyond simple spreadsheets and databases offers value for your employees and the organization as a whole.