In Buffalo, big data drives precision 311 'clean sweeps'
- By John Moore
- Jun 02, 2014
While Chicago, New York and San Francisco look to become national models of the emerging data-driven city, Buffalo, N.Y. has its sights on smaller big data jobs, like filling potholes.
The second largest city in New York State, with a population of 259,384, found itself awash in data after launching a 311 system to handle community concerns. The system fields around 300,000 complaints and issues in a given year and logs all the calls. Potholes, broken street lights, abandoned houses — all the things that can go wrong in a city — are all recorded here.
“I realized I’m sitting on all this data,” said Oswaldo Mestre, director of Citizen Services for the City of Buffalo. “What do I do with that data?”
The answer for Buffalo was to analyze the 311 data and create a density map of different complaints. The map provided a general idea of which neighborhoods needed help. A layer of law enforcement and demographic data completed a picture of the trouble spots. Based on that information, Buffalo has been able to organize a series of “clean sweeps,” in which multiple city and Erie County departments converge on a two- or three-square block area to fix what needs fixing.
Mestre said each clean sweep is different, depending on the issues facing a particular section of city. Buffalo saw 28 clean sweeps completed in 2013, and the city plans to perform 30 such initiatives this year. That compares to the eight that took place prior to the 311 system and its associated data trove.
“When we first started, we didn’t have the 311 system, and we weren’t looking at the data,” Mestre said. “Once we started doing that, it starts to tell you about what is going on in the community.”
Buffalo uses KANA’s LAGAN Enterprise customer service software, a 311 solution, to marshal community data. Resident complaint and service request data is housed in LAGAN Enterprise’s CRM database, according to David Moody, head of worldwide product strategy for KANA, a Verint Inc. unit that specializes in knowledge management systems.
The city can slice and dice this data using KANA’s analytical tools, which includes a physical data mart, pre-built online analytical processing (OLAP) cubes, a library of more than 50 frequently used reports and a report design module for ad-hoc reports.
The KANA system interacts with other city systems to provide a sharper picture. For example, when a citizen’s service request is captured in the CRM database, the location is validated against Buffalo’s geographic information system. The data can then be viewed on maps.
Moody said the approach is typical of many 311 system-related KANA deployments. The system automatically tags a service request’s x and y coordinates, which enables hotspot detection. From the location data, the city can determine which agencies and departments are responsible for providing services, based on the jurisdiction a hotspot falls within.
However, the city’s big data work also revolves around organization. How cities structure themselves to collect, share, analyze and act upon data plays an important role in the success of a big data effort. High-level backing is also critical, according to Moody.
In Buffalo’s case, support for the city’s data effort comes from the top: Mayor Byron Brown. The city’s Division of Citizen Services reports directly to the mayor’s office. Brown emphasizes data quantification and takes a hands-on approach to the clean sweeps, Mestre noted. Mestre’s advice to other cities of big data: Make sure your executive sponsor is in the mix.
“If it doesn’t start at the top ... it’s going to set a different type of tone,” Mestre said.