In Analytics, Use Cases

In previous posts about safety, I’ve talked about how predictive analytics help prevent accidents before they happen. But, analytics are also useful for improving the speed and effectiveness of response when accident do happen.

Here are three ways to apply analytics to emergency response:

Analytics help you identify the real problem

In today’s IoT and Big Data world, you may have multiple systems creating alarms in response to a single accident or event. How do you know what the real problem is? Analytics that bring together alarms from across your enterprise help identify and respond to the real problem.

For instance, your building management system gives you an alarm that sprinklers have turned on in one building of your corporate campus. Does this mean that there is actually a fire, or that someone created a false alarm, or that the sprinkler system has somehow malfunctioned? Without analytics that correlate the sprinkler alarm with other data such as smoke alarms and security video, it’s hard to know.

Analytics help you triage effectively

Alarms tell you that something happened, but by themselves don’t tell you the consequences of what happened. Analytics that include a criticality score for locations, equipment and inventory quickly give you the true magnitude of an accident or event. Knowing the magnitude of consequences also allows you to prioritize your response to multiple simultaneous events.

Continuing with our building fire example, let’s say that a correlated alarm system tells you that there is, indeed, a fire. You immediately want to know the potential consequences of the fire. How many people work in the area? What sort of equipment or inventory is nearby? Are there any guests in the building? Without analytics that rate the consequences of the fire, it’s hard to fully assess the situation.

Analytics help you respond efficiently

Once an accident or event has been properly identified and assessed, there’s no time to waste with inefficient or ineffective response. Analytics that correlate the type of incident with the specific qualifications of first responders and their respective current location means that people arrive on the scene ready to act, instead of ready to assess. The same approach applies to vehicles and equipment that first responders may need to address the situation.

If our building fire is a chemical fire in an inventory warehouse, that situation requires a different type of response compared to an electrical fire in an office building. Without analytics that correlate people, equipment, locations and events, you risk having the wrong people respond with the wrong equipment to handle the situation.



Image courtesy: <a href=’’>stanislaw / 123RF Stock Photo</a>


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