Some fire departments have access to analysts, dedicated data staff, sophisticated technology platforms, and the budget to support all of it. That infrastructure is genuinely valuable and the departments that have built it are doing things with their community risk data that were not possible a generation ago, including data-driven CRR.

Many departments do not have any of that. And if you are leading one of them — a small career department, a combination department, a volunteer agency where you are the chief and the prevention bureau and the data team all at once — it would be easy to read an article about data-driven CRR and conclude that it is a conversation for someone else. A department with more staff. More resources. More capacity.

It is not.

Let me say something directly at the start of this article because it matters for everything that follows.

You do not need an analyst to run a data-driven CRR program.

You do not need a dedicated data team. You do not need sophisticated software. You do not need a budget line item for business intelligence or a staff member whose job title includes the word analytics.

What you need is a question — and the habit of asking it before you make a prevention decision.

The difference between a department that is genuinely data-driven and one that only claims to be is not staff capacity or technical infrastructure. It is whether the people making prevention decisions are looking at what their data actually shows before they decide where to focus, what to fund, and who to reach.

That habit is available to every department regardless of size. The small rural volunteer department with no prevention staff and a chief who works another job during the week has access to the same foundational data-driven mindset as the large urban career department with a full prevention bureau and a GIS analyst on staff. The tools look different. The questions look the same.

This article is about the mindset, the habits, and the technology that together make data-driven CRR real — for departments of every size, with whatever resources they actually have.

What Data-Driven Actually Means

The phrase data-driven appears in strategic plans, grant applications, and conference presentations across the fire service with a frequency that has almost entirely drained it of meaning.

Most departments that describe their CRR programs as data-driven are using the word to mean something like evidence-informed — they are aware of national fire loss statistics, they know that elderly residents face elevated risk, they have read the NFPA research on smoke alarm effectiveness. This is not nothing. Awareness of the evidence base is genuinely valuable.

But it is not data-driven in the sense that actually produces better prevention outcomes.

A data-driven CRR program is one that makes decisions based on local data about local conditions — not national averages applied generically to a specific community. It is one that uses data to answer specific questions about where risk is concentrated, who is carrying it, what conditions are driving it, and whether the interventions being deployed are actually changing anything. It is one where the data is a navigation system rather than a rearview mirror — used to decide what to do next, not just to report what happened.

The distinction matters because national data and local data frequently tell different stories. The national average for residential fire death risk among elderly residents is alarming and well documented. But whether elderly residents in your specific community are carrying that risk at levels above or below the national average — and what specific conditions are driving it in your jurisdiction — is something only your local data can tell you. A prevention strategy built on the national average without local validation is better than nothing. It is also potentially misallocating resources in ways that local data would correct.

Data-driven means local. Specific. Adaptive. And it starts with asking the right questions.

The Data You Already Have

Here is what surprises most small department leaders when this conversation happens: they already have data. More of it than they realize. Collected automatically as a byproduct of doing the job they are already doing.

CAD data is community risk intelligence. Every call your department has responded to in the last three to five years is a data point. The address. The call type. The time of day. The frequency of calls at the same location. The geographic distribution of incidents across your jurisdiction. None of this required any special collection effort — it was captured because you responded to calls and your dispatch system recorded them.

The question is whether anyone is looking at it analytically.

A chief who pulls the top twenty repeat addresses from CAD for the last year and asks why those addresses keep generating calls is doing data-driven CRR. Not sophisticated data-driven CRR. But genuinely data-driven CRR. The pattern is there. The question surfaces it. The analysis — even informal, even conversational — produces intelligence that a prevention decision can be based on.

NFIRS data is community risk intelligence. Every incident report your department has filed contains information about what happened, where it happened, what conditions were present, and what the outcome was. Aggregated across time and geography this data reveals patterns that individual incident reports never suggest. The neighborhood where cooking fires are disproportionately concentrated. The occupancy type generating a share of incidents above its share of the building stock. The call type that is trending upward in a specific population segment.

EMS run data is some of the richest community risk intelligence most departments never analyze for prevention purposes. Repeat EMS addresses. Patient age and condition patterns. Geographic clustering of specific call types. The assisted living facility that is generating a disproportionate share of calls. The zip code where fall-related EMS calls are concentrated in ways that suggest a prevention opportunity. This data is already being collected. It is almost never being used to inform CRR strategy.

The starting point for a data-driven CRR program in any department of any size is not acquiring new data. It is looking at the data that already exists with a prevention question rather than an operational one.

The Questions That Make Data Useful

Data without a question is just storage. Data Ownership Matters. The questions are what turn stored information into actionable intelligence.

Here are the questions that matter most for CRR — and that any department can ask of data they already have:

Where are our calls concentrated geographically? Not which station has the most calls — which specific addresses, blocks, or neighborhoods are generating disproportionate call volume relative to their share of the population or housing stock? That concentration is a risk signal. It deserves a prevention response, not just an operational one.

Who is generating our calls? What do we know about the demographic patterns in our incident data? Are specific age groups, housing types, or population segments appearing in our call data at rates above their share of the community? The elderly resident who is generating repeat EMS calls is not just an operational concern. They are a prevention opportunity — and the data reveals them before the next call happens.

What is changing? Is any call type trending upward over the last one, two, or three years? Is any geographic area generating more calls than it did previously? Trends in data are early warning signals. A neighborhood where residential fire calls have increased twenty percent over three years is telling you something that a snapshot of current call volume does not.

What are our calls telling us about conditions we have not addressed? A cluster of carbon monoxide calls in a specific neighborhood in winter months is a signal about heating equipment and ventilation conditions in that area. A concentration of cooking fires in a specific demographic segment is a signal about the intersection of cooking practices, equipment, and education that a generic fire safety campaign does not address. The calls contain information about root causes if someone is willing to look for it.

Are our prevention efforts changing anything? This is the hardest question and the most important one. If your department has been running a smoke alarm installation program in a specific neighborhood for three years, what has changed in the residential fire incident rate in that neighborhood? If the answer is nothing — or if you cannot answer the question because you have not looked — that is information. It does not mean the program failed. It means the data has not yet been used to evaluate it.

The Human Intelligence Layer

Data systems capture what gets entered into them. They do not capture what crews observe on calls but do not enter anywhere.

This is one of the most significant gaps in most CRR data infrastructures — and it is a gap that no software system fully closes without a cultural and leadership commitment to capturing crew observations systematically.

The company officer who notices that three of the last five calls in a specific neighborhood involved elderly residents living alone — and says so at the next shift briefing — is generating community risk intelligence. The firefighter who observes significant hoarding conditions at an address during a smoke alarm response and mentions it to the captain is generating community risk intelligence. The paramedic who recognizes that a patient’s medication management is deteriorating in ways that are likely to generate another call within thirty days is generating community risk intelligence.

None of this makes it into NFIRS. None of it appears in CAD data. It exists in the observations and conversations of the crews doing the work — and in most departments it evaporates within hours of the call being cleared.

Capturing this human intelligence layer requires two things. First a mechanism — a simple, low-friction way for crews to document observations that belong in the department’s risk intelligence picture rather than just the incident report. This does not have to be sophisticated. A structured field in a report. A brief crew debrief question at the end of a shift. A direct line from company officer to prevention staff that is used and valued. Second a culture — company officers who ask the question, crews who understand that their observations matter, and a prevention bureau that actually does something with the intelligence it receives.

The human intelligence layer is where local knowledge lives. And local knowledge is what makes the difference between a prevention strategy that addresses the actual risk conditions in your specific community and one that addresses a national average.

National Data as Context, Local Data as Strategy

This distinction deserves its own treatment because it is one of the most consequential misunderstandings in CRR practice.

National fire loss data — from the NFPA, the USFA, and related sources — is valuable, rigorously collected, and genuinely informative. Every CRR professional should know it. It establishes the context in which local risk exists. It tells departments what the average American community looks like from a risk perspective. It identifies the populations, occupancy types, and hazard categories that carry elevated risk nationally.

What it cannot do is tell a department whether its specific community matches the national average — or where it diverges and why.

A community with a high concentration of newer construction, strong code enforcement, and an engaged property management sector may face significantly lower residential fire risk than the national average suggests. A community with aging housing stock, high renter density, and limited code enforcement capacity may face significantly higher risk. A community with a large and growing elderly population living in areas without robust social support networks faces a different risk profile than one with similar demographics but strong community infrastructure.

The prevention strategy that is appropriate for one of these communities is not appropriate for all of them. And the only way to know which community you are actually serving — rather than which community the national data describes on average — is to look at your local data.

National data tells you where to look. Local data tells you what you are looking at.

The department that makes prevention decisions based primarily on national statistics is doing something better than nothing. The department that uses national data as context for understanding its local data is doing something genuinely strategic.

The Technology That Makes This Scalable

Everything described above is possible with the data and tools most departments already have. A motivated chief with CAD access and a few hours of focused attention can begin to answer the questions that make a program data-driven. That is true and important and worth saying clearly.

It is also true that technology makes this work significantly more accessible, more consistent, and more scalable — particularly for small departments without dedicated prevention staff or analytical capacity.

The technology that matters most for data-driven CRR falls into three categories.

Assessment platforms that collect local data systematically. A digital home safety assessment platform — whether delivered in person through a structured crew interaction or distributed digitally via QR code, social media, or community outreach — generates address-level risk data at a scale and consistency that manual processes cannot replicate. Every completed assessment is a data point. Every set of data points from a neighborhood is a pattern. Every pattern across neighborhoods is the local risk intelligence picture that makes prevention strategy genuinely local rather than generically national.

For small departments this matters specifically because it removes the dependency on analytical staff. A platform that aggregates assessment data, identifies patterns, and presents findings in a format that a chief or company officer can understand and act on without a data science background is doing the analytical work that the department does not have capacity to do manually. The barrier to data-driven CRR drops significantly when the infrastructure exists to capture and organize the data automatically.

Mapping and visualization tools that make patterns visible. Data that exists in a spreadsheet or a database is useful to people who know how to interrogate it. Data that is mapped geographically — showing where incidents are concentrated, where assessment findings cluster, where the gap between risk and prevention coverage is largest — is accessible to anyone who can look at a map. Most modern CAD and records management systems have basic mapping capability. GIS tools that were once available only to large departments with technical staff are now accessible to small departments at low or no cost. The question is whether anyone is using them to look at prevention questions rather than just operational ones.

Reporting infrastructure that connects data to decisions. The most important technology investment for a small department may not be a sophisticated analytics platform. It may be a simple, consistent reporting template that asks the right questions on a regular schedule — monthly or quarterly — and puts the answers in front of the people who make prevention decisions. A chief who sees a one-page summary of their top repeat addresses, their call type trends, and their prevention program reach every month is making better decisions than one who has access to all the same underlying data but never looks at it in an organized way.

Technology does not replace the mindset and the habit. A sophisticated platform in a department that has not built the practice of asking prevention questions before making prevention decisions will produce data that nobody uses. The mindset and the habit come first. The technology amplifies what they make possible.

Where to Start

If this article has described a gap between where your department’s data practice is and where it could be — here is the most honest advice about where to start.

Pick one question and answer it with data you already have.

Not ten questions. Not a comprehensive data audit. One question that is directly relevant to a prevention decision your department is facing or should be facing. Where are our residential fire incidents concentrated geographically? Who is generating our repeat EMS calls? What call type has been trending upward over the last two years?

Pull the data. Look at it. Talk about what it shows. Make one prevention decision based on what you find rather than on assumption or tradition or what worked in the department you came up through.

That is data-driven CRR. It is available to you right now. It does not require an analyst or a sophisticated platform or a budget line item. It requires the habit — and the willingness to let what your data actually shows change what you actually do.

Everything else — better tools, more sophisticated analysis, technology that scales the work — builds on that foundation. But the foundation is a question and the discipline to answer it honestly before you decide.

That is where it starts. For every department. Of every size.

Brent Faulkner, MAM, FO, is the CEO and Founder of Virtual CRR Inc.
A retired Battalion Chief from Anaheim Fire & Rescue, Brent brings 28 years of fire service experience, including leadership in structure fires, wildland operations, hazardous materials response, EMS incidents, and specialized rescue operations. He also served 17 years on a Type 1 Hazardous Materials Response Team.

A defining moment in Brent’s career came while leading Critical Infrastructure Protection (CIP) efforts at a DHS-recognized Terrorism Fusion Center. There, he oversaw initiatives to safeguard critical infrastructure from terrorism, natural disasters, and emerging threats — an experience that shaped his passion for Community Risk Reduction and ultimately led to the creation of Virtual CRR.

Brent holds a Master’s Degree in Management, a Bachelor’s in Occupational Studies, and Associate Degrees in Hazardous Materials Response and Fire Science.