Oscar the Grouch Won’t Stand For Inadequate Garbage, And Neither Should You Settle For Inadequate Data Management Systems
So you want to steer your agency towards Intelligence-Led Policing? The first thing you need to look at is your data set. Over and over again I see agencies hastily purchase mapping or other intelligence software, attempt to put it to immediately use, and then disagree with the output they receive. The common theme I often find when reviewing their data sets in these scenarios falls under the age-old category of old garbage in, garbage out.
So if you fall into the category I just described, you are by far not alone. As a matter of fact, I would venture to say that you are in the majority. I even made the same mistakes when I began our Intelligence-Led Policing initiative. I remember I was so excited to get going with CommandCentral that I never bothered to really analyze the data that I was putting into the system. After all, I had been using our records management system for 20 years, surely the data was correct – right? What I found was a resounding NO. It was not good data. Now don’t get me wrong, the basics of the data were correct – the type of crime, suspect, victim, things like that were solid. What was not so correct, however, was our mapping data and how our crime types translated into CommandCentral. Let’s camp out on those two topics and discuss a couple of things that you can do to turn your bad data into good data.
First Let’s Talk About Mapping
Very few mapping systems, whether you are using GIS or some other type of mapping system, are always spot on. The reasons for these inaccuracies vary widely. From inaccurate GIS mapping at the onset, to duplicate addresses in your city that are only separated by a North-South or East-West designation, or simply a data entry mistake. Although I could not change these map points in my records management system, I could change them in CommandCentral. With just a few steps I was able to take my map, with an average of 150 inaccuracies a month, and turn it into a completely accurate crime map, with no inaccuracies.
Allow me to explain the process; in CommandCentral, you are able to look at one or all of your zones or beats using the “Area” tab. Within the Area tab, there is a sub-tab called “Outside Area.” Here you can see all of your calls that populated outside of your agency’s physical boundary zones. And since the system allows you to manage the details of each incident coming from your RMS, you can simply pull up the incident on a map, and with click and drag functionality, pull the the incident point to the appropriate geographical location. This process, which you will become very proficient at, will allow you to present your maps without excuses and mistakes.
Accurate Crime Types Make A BIG Difference
Now that we have fixed your mapping problem, let’s talk about making sure your crime types are in the proper categories in order for you to get the proper intel. Depending upon country, state, or locality, crime terms can vary widely. For instance in my state, we don’t use the term larceny, we use theft instead. We also don’t use the term embezzlement, but rather we use a variety of codes under fraud. While our records management systems seem to “do it all,” it is our duty to make sure that our information is laid out correctly with in our systems. In CommandCentral, there is a crime tab for “other;” this tab is used for information in a records management system that might not fit into a typical crime category. While this can be a useful tab, I more often than not observe other agencies use this for crimes such as robbery, shooting, and thefts, in the “other” category. Simply put, if the data is in the wrong category when you run a report on a specific crime type, you will be mis-reporting and presenting inaccurate information in your final report. Most importantly, you won’t be able to deliver an intelligence product that allows your command staff to make actionable decions in confidence.
This year let’s make sure to strengthen our intelligence by cleaning up our bad data. As always, if you have any specific questions or comments, or if you need deeper instruction on how to clean up your data sets, don’t hesitate to contact me at daniel.seals@publicengines.com.
