A while ago in this space we talked about how big governments are having great success with the concept of “big data” and data anlytics. A recent story in the news provided an example of this.
An article on “New York City Fights Fire with Data” by Brain Heaton (www.govtech.com/public-safety/New-York-City-Fights-Fire-with-Data.html) appeared in Government Technology on May 15. It talked about how the New York City Fire Department (FDNY) worked over a period of several years to develop the FireCast algorithm. FireCast draws on computerized data from five City of New York agencies and considers 60 risk factors to identify buildings that are most vulnerable to fires.
The development of the system dates back to 2008. Bringing the Department to this point required automating and centralizing the department’s building inspection workflow. Previously, the inspection process was largely paper-driven and was decentralized at each firehouse.
A great payoff of the system is the ability to analyze historic data on buildings that ultimately did have fires to determine how analytics might have used data already on hand to have predicted which buildings were likely to have the fires. Of course, this retroacitve knowledge can then be put to work to fine-tune the algorithm to more precisely predict future fires and then take actions such as more targeted inspections and enforcement.
This is a great story of a valuable return from a technology investment. But getting to this point required resources such as a full-time “data scientist.” So this still leaves us asking the question we asked a while ago—what does this mean for our thousands of smaller governments who do not have the resources a New York City has?
In a previous column we also talked about some fertile areas for analytics work in smaller governments. These included things such as police and fire response times, processing of service requests, identifying dangerous intersections, and issues with nonpayment of property taxes. But again, how can smaller government do analytics work in these areas despite their limited resources? In our next column we’ll talk about analytics tools that could be used by governments big or small to work with their data in a “big data” kind of way.
Until then, have you had any success with data analytics in your smaller government? If so, please comment and tell us about your accomplishments. Or, let us know if you’ve tried to work in this area and experienced any frustration, and why.