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Cities Are Having a Data and Analytics-Driven Moment, and It’s Likely to Stay

The term 'big data' may imply that data collection and analysis is a simple process, but time and cost have told local governments otherwise.

This story was originally published by Data-Smart City Solutions.

Cities worldwide are having a data and analytics-driven moment, and it’s one that is likely here to stay. Thanks to advances in computing, code-sharing, and mindsets around accessing government data, it has never been a more affordable, accessible or effective time to start harnessing analytics capabilities to improve local government services.

Yet this does not always mean that analytics can be deployed without much effort, as oft-repeated buzzwords like “big data” may sometimes imply. Leery of such promises, some local governments may suspect that the time and cost it takes to undergo analytics efforts is simply not worth it. Others may simply ask: why analytics?   

“It is estimated that 2.5 quintillion bytes of data are created each year,” Jennifer Bachner, Senior Lecturer at John Hopkins University, notes in her new book Analytics, Policy, and Governance. “In a governance context, the real value of data is the policy implications we can glean from the results of an analysis.” 

Indeed, local governments have been experimenting with a host of strategies and models to find ways, given limited resources, to effectively find that value and implement analytics programs within their cities. New leadership roles and divisions—such as Chief Data Officers and Innovation or Performance Management Offices—barely existed a decade ago, but are now becoming ubiquitous among larger cities.

“Being a data-driven city is about more efficiently and effectively delivering the core services of the city,” former New York Chief Analytics Officer Michael Flowers noted in the 2013 book Beyond Transparency. “Being data-driven is not primarily a challenge of technology; it is a challenge of direction and organizational leadership.”

As no two cities are alike, analytics approaches have differed from city to city. In New York, for example, the city’s Finance Department has used analytics to increase the productivity of auditors reviewing companies thought to be underpaying their taxes.  By algorithmically identifying individuals who had businesses similar to others, but who stood out as outliers on taxes paid, the team reduced its portion of audit cases closing without any changes from 37 percent to 22 percent, giving the department a 40% percent increase in productivity over a three-year period.

In New Orleans, the city has been saving lives by using data to predict which of the city’s buildings need to be equipped with fire alarms.  Using data collected by the Census and New Orleans Fire Department, the city identified building age, building inhabitant income, and building inhabitant occupation length as strong predictors for determining if a structure may not have a smoke alarm installed. It then mapped this information along with fire risk calculated from resident age data and fire data over the previous five years.  The program’s results now inform NOFD’s door-to-door program to install free smoke alarms.

And in Detroit, blight issues are being addressed by using analytics to help identify candidate structures for demolition.  The city’s Blight Removal Task Force deployed more than 200 people over a 14-week period to survey more than 99 percent of the city’s 380,217 properties. Information collected onsite, including photographs and notes on lot characteristics, structure conditions, and ownership status, is then sent wirelessly to the operations center, where it is analyzed while the team is still at the property. This information has helped the city promptly identify and address areas of elevated safety concern.

And perhaps most importantly, the more cities and local governments innovate, the more others can replicate. The City of Chicago, for example, has successfully developed and deployed an analytical model that has considerably enhanced its food inspections processes, with inspectors now identifying potential violators 25% faster. The model, developed using open-source tools and with support from Bloomberg Philanthropies, has been replicated and deployed by metro-DC’s Montgomery County, Maryland, and has been gaining attention from other governments across the country.

These examples are part of that growing movement that Data-Smart City Solutions has been researching, disseminating, and sharing with readers across the country and world. Echoing Flowers’ observations, it’s not just the technology that’s brought positive results, but the willingness and ambition of these cities—and many others—to take on innovative approaches. 

Sean Thornton is a Program Advisor for the Civic Analytics Network at Harvard's Ash Center for Democratic Governance and Innovation, and writer for Ash Center publication Data-Smart City Solutions. Based in Chicago, Sean holds joint Masters’ degrees from the University of Chicago in Public Policy and Social Service Administration. His work has spanned the city's public, philanthropic, and nonprofit sectors.