I’ve spent most of my career running toward emergencies. Sirens wailing, gear on, adrenaline pumping—that’s the part of the job everyone sees. But in recent years, I’ve come to realize that some of our most important work happens quietly, long before the tones ever drop.
That quiet work is data analysis.
And it’s changing how we fight fires—not just when they happen, but before they even start.
Seeing Patterns in the Numbers
For decades, fire departments have recorded every incident: the time, location, cause, and outcome. Those reports often went into storage or were used only for compliance. Now, with platforms like EPR FireWorks’ Unified RMS and integrated business intelligence tools, that same data has a second life—fueling predictive analytics.
When you feed years of incident data into a system that can actually connect the dots, you start spotting patterns. Maybe certain neighborhoods have more electrical fires in winter. Maybe the number of kitchen fires spikes during certain holidays. Or maybe one block sees repeated calls for the same building because of faulty wiring.
It’s like lifting the fog and seeing the risks before they flare up.
Identifying High-Risk Areas and Times
I remember one project where we layered five years of fire call data onto our city map using Esri integration in our RMS. The picture that emerged was eye-opening: clear clusters of incidents in specific areas, and peaks in activity during certain weeks.
Armed with that knowledge, we could:
- Schedule targeted fire inspections in those hotspots.
- Launch public safety campaigns right before the high-risk periods.
- Increase patrols or readiness in certain districts during known danger windows.
We weren’t just reacting anymore—we were anticipating.
Better Resource Allocation
Predictive analytics isn’t just about prevention—it’s about being ready when prevention fails.
If the data shows a high probability of incidents in one area during a certain month, we can adjust staffing, apparatus placement, and even hydrant testing schedules to match. It’s a lot like weather forecasting—you position your crews where the storm is most likely to hit.
I’ve seen this save minutes on response time, simply because the right units were already in the right place at the right time. And in our line of work, minutes matter.
Community Prevention Programs Powered by Data
We’ve also used analytics to guide our outreach programs. One year, our reports showed that a surprising number of fires in a certain neighborhood started from space heaters placed too close to bedding.
Instead of just issuing a general “fire safety” message, we designed a targeted campaign for that specific risk. We partnered with community centers, handed out free heater safety kits, and even went door-to-door in some blocks.
Within a year, incidents from that cause dropped noticeably. That’s the power of data—you’re not just guessing what people need to hear; you know.
The Technology That Makes It Possible
To do predictive analytics well, you need more than spreadsheets—you need a unified, integrated platform that can pull from multiple sources and present the insights clearly.
Our EPR FireWorks RMS is the backbone of this. It stores all incident data, integrates with mapping tools, and connects with inspection and prevention modules. Then, using the built-in business intelligence system (with over 270 pre-built reports), we can slice and filter the data to find exactly what we’re looking for.
The best part? The platform updates in real time. That means new incidents immediately influence future risk models.
Steps to Implement Data-Driven Prevention
If you’re thinking of building your own predictive analytics program, here’s what I’d recommend based on our experience:
- Centralize Your Data
Make sure all your incident, inspection, and community risk data live in one system—preferably one with strong reporting tools. - Map Your History
Use GIS mapping to visualize past incidents. Patterns jump out faster when you see them on a map. - Look for Seasonal and Recurring Trends
Many risks aren’t random. Compare year-over-year numbers to see if certain times or events create spikes. - Tie Insights to Action
Don’t just admire the data—let it shape your inspections, public outreach, and staffing plans. - Review and Adjust Regularly
Risk profiles change as neighborhoods grow or hazards shift. Keep your analysis current so your strategy stays sharp.
Why This Matters More Than Ever
In emergency services, we pride ourselves on rapid response. But the truth is, the best emergency is the one that never happens.
By using historical data and predictive analytics, we’re not just responding faster—we’re preventing tragedies altogether. We’re identifying hazards before they ignite, deploying crews where they’re needed most, and educating communities about their specific risks.
It’s a shift from being purely reactive to becoming proactive protectors. And it’s one of the most meaningful changes I’ve seen in my career.
When I put on my turnout gear, I’m ready for the call. But thanks to the data, there’s a good chance that call might never come—and that’s a victory you can’t measure in minutes, but in lives and homes kept safe.
