Fighting the ‘Data Delgue’ in Smart Firefighting

ESO Staff

Firefighting is, as an industry, getting “smarter.” And it’s a good thing, too. With more complicated buildings and properties, more chemicals and hazards, and more people in our cities and communities needing assistance, a fire department owes it to its team to take advantage of all the technological advances available that could help save lives and property. 

From PPE that can deliver second-by-second biometric readings on each team member, to detailed pre-inspection digital files and photos, to bird’s-eye views of a scene from a drone, and improved fire field situational awareness – like temperatures behind closed walls or levels of hazardous chemicals in the air – the amount of information available to incident command at any given emergency is almost unlimited. 

In the same way, many of today’s firefighting tools and technologies can store historical data, offering the chance to thoroughly review an event, or a time period of operations, to make observations, and to more easily complete audits. This, in theory, makes it easier to identify gaps or areas for improvement, and to ensure your staffing and equipment needs are fully met. 

However, the flip side to this unending flow of rich data is the very real danger of the so-called “data deluge.”  Just like you can have “too much of a good thing,” it’s very easy to be overrun with large amounts of data, especially in its raw form. You know the data is there, you know it has the potential to help you make better decisions, and you know it’s probably changing and updating by the minute. But how can you get your hands around it to make it work for you? 

The first step in understanding how to make your data work for you is understanding the key terms involved in the process. In a recent article, mobile app data company Localytics described the three key phases of information processing – data, analytics, and insights – with the analogy of looking at an impressionist painting. When you are standing very close to the painting, all you see is colors and blobs and shapes. When you back away a few feet, you start to see more structures, as the colors and shapes begin to blend into recognizable figures. Taking another step back, you begin to see the entire painting and understand the overall picture. In the same way, when working with raw data, you must progress through similar steps – from data to analytics to insight – to get useful conclusions. 

Step One: Data 

You receive a mountain of data points from a wide range of channels. This data is available streaming live during an incident, or in a collected virtual receptacle for use later. Some of this data is helpful at the moment, while other information can be reviewed later to improve processes and handshakes, or to examine what went wrong at an event. It’s important to have a solution in place for storing the data you are receiving, like the “cloud” or a SaaS, that can be accessed from anywhere at any time. 

Step Two: Analytics 

Now the number-crunching must begin. With your seemingly unlimited amount of data, you must discern what variables you wish to examine and what factors might be involved. Do you want to review response times? Maybe you want to see how your individual team members are responding to physical stressors. Perhaps you want to refine you operating procedures for rerouting traffic in specific parts of town. The possibilities of what can be improved are endless thanks to the collection of data at past events. This is stage where it’s extremely helpful to have the proper software tools to search your data stores and cross-examine the variables in which you are interested; it’s simply no longer feasible or efficient to do so by hand or with simple office software tools like Excel. 

Step 3: Insights 

Here is where the data becomes decisions. Some call it the “light bulb moment,” where you recognize key takeaways that are actionable and that can really make a difference in your day-to-day operations, or your long-term plans. These key insights – or “metrics” as they’re sometimes called – can not only help you make your case for spending on new equipment or staffing, but can help you form goals for your upcoming year. These insights help identify a need and set the bar for comparisons, helping you see if your changes are making a difference and, if not, allowing you to tweak your solutions. The changes you propose can be supported by the insights, giving you a strong case for making improvements, on both a small and large scale. 

The data is there, waiting to be another tool in your arsenal for making your fire department more efficient, more effective, and overall safer for both your team and your community. Understanding the process it takes to get from data to decision, and ensuring you have the proper tools to do so, is key in making the most of today’s smart firefighting. 

Leave a comment

Leave a Reply

Your email address will not be published. Required fields are marked *