Governments today operate in an increasingly complex world, reflected in the volume and ubiquity of data produced by citizens and agencies, as well as the computing power to analyze it. In order to better understand and respond to citizens’ needs and allocate public resources more efficiently, governments must use predictive analytics to leverage this data and develop innovative solutions to contemporary urban challenges.
Predictive analytics is the use of historical data to look for patterns and identify trends, which can be used to reorganize service delivery, anticipate future needs and prevent potential problems. The Inter-American Development Bank (IDB) is working to accelerate the uptake of predictive analytics by governments in Latin America and the Caribbean, spurred by success stories in Montevideo, which is using crime data to analyze patterns of criminal behavior to identify critical points of crime and better target police efforts, and Mexico, which is using data on electricity consumption to get near real-time forecasts of economic activity to inform policymaking. Part of this effort includes launching the “Innovations in Public Service Delivery” discussion paper series, which provides a framework, toolkit and roadmap for public sector employees looking to implement data-driven initiatives. Susan Crawford, Benjamin Weinryb Grohsgal and I contributed the fourth installment of this article series, focusing on challenges, successful case studies and next steps for public officials using predictive analytics.
A number of non-technical, structural obstacles stand between governments and the ability to leverage data for prediction and problem solving. In order to overcome these barriers, governments must:
The discussion paper analyzes in detail two case studies:
City of Chicago: Chicago piloted a predictive analytics project to enhance operational outcomes, specifically in the area of better-targeted rat baiting. By mining the city’s 311 data, a team from the Department of Innovation and Technology (DoIT) observed a relationship among 311 call types that historically correlated with rat infestation problems. The DoIT team took this predictive model to the Department of Streets and Sanitation (DSS) and mapped specific areas for intervention, some of which the DSS had not planned to target. Following the model, the DSS discovered the largest infestation it had ever seen. Thanks to analytics, the agencies were more transparent about what they were doing, resulting in an effective collaboration to resolve a major city problem.
State of Indiana: Indiana launched a Management and Performance Hub (MPH) that facilitates the use of analytics across state databases. In order to better understand the correlation between infant mortality and indicators like health care, nutrition and housing, MPH integrated government data from family, health, finance, business and employment agencies to reveal more obscure trends. Caseworkers could compare a family’s information to past and present data on at-risk families, making a risk estimate that helps determine the probability that a child will be harmed in the future. In this case, the results of the analytics project empowered caseworkers to make data-driven decisions in their efforts to address child welfare issues.