Leading and lagging indicators are metrics that are widely used by Environment, Health & Safety (EHS) professionals to measure performance and drive outcomes. EHS departments are collecting more types of data and the quantity of data captured is only increasing, thanks in part to data from mobile and drones. Having a lot of data is a good thing. What’s even better is using powerful business intelligence (BI) tools designed to turn data into insights, resulting in more effective decision making and improving EHS management beyond the capabilities of leading indicators.
“Most firms have vast amounts of EHS and other operational data. The capability to sift through this data and find relevant information and turning it into prescriptive actions will lead to healthier, safer and more productive workplaces. The key? It’s advanced data analytics tools that enable business intelligence.”
Christine Adeline, Senior VP of Product Management and Product Marketing, SAI360 EHS&S
Business Intelligence, or BI as we know it today, has a longer history than you might think; one that predates computers and other technological advances. In fact, the first term of ‘business intelligence’ appeared in 1865 by Richard Millar Devens in his Cyclopaedia of Commercial and Business Anecdotes in that year.
Devens used the term to talk about how a banker, Sir Henry Fumese, used “business intelligence” to gain an advantage over his competitors. Through careful study, he gained knowledge of the political issues, risks, and general market conditions of that time. And there you have the first recorded use of BI.
It wasn’t until the 20th century with the emergence of computers that BI started to gain recognition. The 50s, 60s, and 70s came and went and with them the development of various enterprise applications. But these were one-dimensional reports. The problem was that the underlying data in these reports was siloed; there wasn’t a single, consistent, holistic view of the data.
Fast forward to 2023
Data is now being captured from connected devices in real-time and then analyzed for insights in real-time. The breakthrough is in speed, quality and quantity of the data, along with advanced analytical and visualization tools that equip EHS professionals to make data-driven decisions with a high degree of accuracy.
From a data science modeling perspective, more data produces higher quality predictive analytics. With EHS, more data is coming in from mobile devices, monitors and drones. All this data fuels insights for risk prevention. Before a near-miss or incident occurs, data and predictive insights equip EHS with advance notice to address safety.
Today, many large organizations use AI, machine learning and advanced analytics as key instruments to identify, among other things, patterns in customer buying behavior. This technology can also be used to predict future stock requirements and detect fraud. To do this, organizations must first collect large volumes of transactional data and then apply sophisticated statistical algorithms to find patterns and anomalies. All of this is made possible due to process automation making more data available and the continual decrease in the cost of computing power.
Your Employees’ Safety, in 1s and 0s
When it comes to the EHS realm, with its enormous amounts of data stored across the organization, there is a need for a centralized EHS data warehouse enabled by a robust BI. Buried in this warehouse are patterns, awaiting discovery and analysis with advanced AI tools. All this data, once analyzed, can lead to data-driven insights that will improve decision-making when trying to determine where future incidents could occur.
EHS professionals are looking for ways to predict and prevent significant incidents. Traditionally, using statistical methods on historical data to determine what happened and why gives them a good indication of past performance, such as how many incidents of each type have occurred. This allows them to establish what has caused them. Using this information, an organization can learn from previous events and put controls in place to eliminate or reduce hazards and mitigate risks.
So EHS and BI are the perfect bedfellows? Yes!
Moving Towards Operational Excellence
EHS is highly regulated. Organizations need to measure and record significant amounts of data to ensure and prove their compliance to governing authorities, such as incidents, near misses, absences due to health and safety accidents, safe behavior observation, inspections, and root cause analysis investigations, just to name a few.
In most cases, this information has been stored and accessed in isolation to tactically improve business operations. However, mature organizations are combining their historical data with real-time data and applying predictive analytics to identify potential incidents that may happen in the future along with probabilities. When implemented properly with employees being able and willing to report near misses and incidents, this is a good method of predicting certain types of incidents.
However, to prevent incidents, EHS professionals must digest the results of predictive analysis and determine the correct courses of action.
To fully benefit from BI predictions, EHS systems should ideally go one step further than just forecasting what could occur by suggesting actions that should be taken along with any associated consequences of each option given. This prescriptive analysis can help an organization make decisions around maximizing future opportunities or give the best chance of mitigating any risks with which they are faced.
More Data, Fewer Incidents
Advanced BI provides many benefits to EHS. One example is more reliable hazard detection processes. Rather than relying solely on human expertise, advanced analytics and machine learning is being used to detect issues resulting in improvements in accuracy.
BI also gives EHS departments a more reliable way of sharing best practices. If we look at best practices in risk detection as an example, they are being codified into computer algorithms which can be easily shared across multiple geographies and divisions. This results in greater consistency across the organization, as employees do not have to be relied on to share best practices anecdotally with varying levels of success.
Advanced BI results in an increase in the detection of risks that will lead to a decrease in the number of serious incidents.
The Road to a Safer Working Environment
Having the ability to quickly turn raw data into actionable information can make the difference between smart EHS decisions and costly mistakes. While making sense of all the information from different data sources can be a challenge, it is imperative to have the right analytical tools in place. Put data to work enabling easy identification of insights and instilling the confidence to act, ensuring focus remains on EHS performance improvement.