Predictive Analytics involves applying statistical algorithms and artificial intelligence (AI) to help identify the likelihood of future outcomes based on historical data. Using these tools, organizations can leverage their safety data to predict what types of events are most likely to happen, and identify the circumstances around how they might happen, helping inform incident prevention efforts through better decision-making, prioritization, and resource use.
But how do you shift your organization towards a predictive analytics approach to injury prevention? Where do you start, and what do you need to know? For organizations interested in incorporating predictive analytics into their injury prevention strategy, we’ve outlined some recommendations and best practices that should be considered to ensure your analytics approach is effective and sustainable.
There’s little point investing in a predictive analytics program if you’re unclear what exactly you’re trying to achieve from it. Any predictive analytics program starts with identifying your program objectives. A few questions to ask yourself:
- What specific problems am I trying to solve?
- What outcomes do I hope to achieve?
- How do these HSE objectives align to bigger organizational priorities and goals?
- What resources will be needed – at all organizational levels – to build and sustain the program over time?
Defining your program objectives is critical because it will help you determine what types of safety data sources you’ll need to support predictive modeling. And that means you’ll likely want to collect a variety of safety data, both leading and lagging indicators, to help build the most comprehensive picture of operational risk that can be considered by the analytics engine. If you’re unsure what type of safety data to collect, start by looking at your historical incident data. Consider the following:
- What types of incidents are most prevalent?
- Are there particular trends or common causes from these events?
- If so, what kinds of actions could be taken proactively to address the causes or contributing factors?
Those data points will form the basis of leading indicators you might want to track and introduce into your data analytics approach.
For instance: if incident reports reveal that most injured workers were not properly trained, you might want to include training data into your analytical model. By identifying which employees are lacking requisite training, and even associating training status to the level of risk in the tasks they perform, you may be able to identify those at greatest risk of injury.
To learn more, check out the eBook, How to Introduce Predictive Analytics Into Your Incident Prevention Strategy. The ideas and recommendations provided by Cority’s health and safety experts are intended to provide you with a logical starting point to explore how you leverage and unlock the full value of your safety data to detect and prevent workplace harm before it happens.
