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GCN : June 2014
GCN JUNE 2014 • GCN.COM 27 with Booz Allen Hamilton's Strategic Innovation Group, said they also need an overarching strategy and an orga- nizational structure to pursue big data programs. BIG DATA MODELS Mills sees three primary models cities can use when taking on data analytics: centralized, diffused or deployed. The centralized model keeps an ana- lytics brain trust in a central group, while the diffused approach embeds expertise within each government business unit or department. The deployed model uses a centralized team, which dispatches in- dividual members to departments who need help tackling problems. Chicago, meanwhile, plans to help cit- ies over the resource hurdle. Catlett said certain cities have some of the capabili- ties required to develop an analytics ser- vice, but lack the resources to assemble it all from scratch. For those cities, the open source data analytics platform would provide a key piece of infrastructure. Catlett said the platform may also be made available as a virtual machine via the Amazon Web Services cloud. Other cities may not have sufficient resources to maintain an analytics sys- tem regardless of its source. For them, Catlett said commercial services like Socrata might be the best way to get started, Catlett said. Socrata, a cloud- based offering, provides an open data portal and tools for performing data analysis. Cities will also be able to load their data into Chicago's data analytics sys- tem. "That idea of sharing the platform with smaller cities is very much a part of the design," Catlett said. • 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. EXECUTIVE SUPPORT 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 of ce. Brown emphasizes data quanti cation 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. -- John Moore This push towards transparency has created a real opportunity for those of us who want to understand cities. -- CHARLIE CATLETT, ARGONNE NATIONAL LABORATORY / UNIVERSITY OF CHICAGO