By Philip Duczyminski
Businesses have been using data to make decisions for many years now.
The data they use help them analyze their business practices and help them become more efficient and more profitable, hire better employees, and market their products more effectively. In 1994, Compstat was effectively used within the New York Police Department (NYPD). Compstat stands for COMPuter STATistics and is a management process within a performance management framework that synthesizes analysis of crime and disorder data, strategic problem solving, and a clear accountability structure.1 Compstat has been credited with reducing crime in New York as well as other jurisdictions, but would the premise behind the system work for fire departments as well?
First and foremost, this article will detail some of my personal experience with Compstat as well as provide some recommendations to make the process more suitable for fire departments. In 2009, the Novi (MI) Fire Department hired the International City Managers Association (ICMA) to perform a staffing and utilization study. In the study, ICMA recommended implementing regular Compstat meetings.2 Since that time, the department began conducting a weekly meeting between the police and fire departments with the weekly data presented to both agencies. One of the biggest things I have learned is that data usage is different for every agency. A police department can use data to find crime trends, accident trends, etc. These data can help determine where to effectively put law enforcement officers to reduce these incidents. A fire department will not necessarily reduce fires or medical incidents by placing more firefighters in a given area. However, there can be some benefits if the data are used correctly and ethically.
Why Look?
So, why would you want to look at your data? The simple answer is to improve. Did you know that 85 percent of all statistics are made up? If you do a search across the Internet, you will find thousands of statistical inconsistencies. Why would that occur? Simply put: When someone is trying to prove a point, he needs some kind of data to prove his point. Mark Twain said, “Facts are stubborn things, but statistics are pliable.”3 I know this point may cause some unrest for some, but let me ask you one more question: Have you ever seen someone try to prove his point with data and find that the opposite fact is true? Me either. Within a selection of data, there are many variables that will generally allow the person selecting the data to keep, change, alter, or eliminate data or just change the criteria to keep data out of the report that are detrimental to his cause.
I know of an emergency medical service (EMS) agency that contracts for service with a local municipality that is contractually obligated to meet a response time standard. The EMS agency must arrive to priority calls within 7 minutes 30 seconds 90 percent of the time. This looks pretty good on the surface, but if you look at the data you will find that the agency is allowed exceptions from the standard. It is allowed an exception for instances such as multiple calls at the same time, poor road conditions, etc. Once it subtracts all the calls it determines to be “exceptions,” that is its average response time. In my opinion, this shows that the data can be manipulated by many variables to ensure the agency is meeting its contractual requirements. There should never be an emergency call that is considered an exception. I am not making the argument about which service is superior. The point I am trying to make is that the numbers don’t tell the whole story, and many people will use those numbers to prove their point even if they are flawed. Residents don’t want exceptions, they want service. As George Canning said, “I ca