Governments are increasingly using Big Data and data science to address pressing social problems and improve public services. In many cities, data analytics is the underpinning for programs that map blighted urban areas for redevelopment, smart energy grids that automatically adjust streetlights to conserve energy, and crime prevention systems meant to deter crime and improve safety. Despite these efforts, government still lags behind the private sector in its embrace of data.
On September 25, 2016, just weeks ahead of the general election, Bloomberg’s Data for Good Exchange (D4GX) 2016 conference will showcase the larger role data science can play in “better governance” and public-sector decision-making and policy planning. Data scientists and representatives from industry, government, non-profits and academia will convene at Bloomberg HQ in New York City to discuss the power of data and predictive models to enhance government effectiveness and scale social-impact programs.
“One goal of the conference is to provide more use cases to illustrate how cities can take advantage of data, if it’s not already on their radar screen,” says Gideon Mann, head of Data Science at Bloomberg. He points to New Orleans and San Jose, California as examples of city governments making headway in mining data “to fundamentally change how government service happens.”
Gun violence, which kills more than 30,000 Americans every year, is a priority area for data analytics. However, despite gun data being regarded as a powerful crime-fighting tool, few cities today use it to shape public policy. Everytown, the nation’s largest gun-violence prevention organization, will host a panel discussing the importance of gun data and highlighting programs like the one run by the University of Chicago Crime Lab, which traces gun sales to fight the illegal trade of firearms.
Keynote speakers DJ Patil, Deputy Chief Technology Officer for Data Policy and Chief Data Scientist in the Office of Science and Technology Policy, and Lynn Overmann, senior policy advisor to the U.S. Chief Technology Officer at The White House, will talk about data science in federal government programs like the Precision Medicine Initiative, which uses data to improve personal healthcare delivery.
Papers submitted to D4GX 2016 offer insights into data-driven projects already underway. For example, they showcase data’s role in analyzing tourism dynamics, eliminating cyber threats and fighting human trafficking. They also look at strategies for leveraging telecoms data for malaria interventions and the impact of data-driven risk assessments in the Flint, Michigan water crisis.
Massive data collection and greater reliance on machine learning algorithms also pose risks. Because algorithms are designed by people, they can convey bias and discrimination in applications, such as judge sentencing guidelines and credit-score ratings. Keynote speaker Cathy O’Neil, a data scientist and author of the recently published book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” will explore these concerns and how they may exacerbate inequality in our society.
For all the promise of Data for Good, as the movement is known, government has been slow to incorporate data into policymaking. One reason is the high cost of upgrading legacy technologies and infrastructure to handle data liquidity – the ability to deliver massive streams of data through systems easily and securely.
In this election year, data science may yet get a boost, if new political leaders implement more data-driven policies and fact-based solutions. “Data will help us understand problems in a deeper, more concrete way, and find answers that are non-partisan,” says Mann. “We will be able to provide more efficient services and a better life for everyone.”