• Program 2015-16

Big Data and Sustainable Development

Date: 

April 14, 2016

Time: 

6:00 - 8:00 pm

Host: 

NYU Stern Center for Sustainable Business

Venue: 

NYU Kaufman Management Center, 44 W 4th Street, Room M2-60

Big data presents big challenges—and big opportunities. Referring to the seemingly infinite quantity of information now being recorded by recently developed technologies across a multitude of sectors—health care, transportation, finance, marketing, social media, agriculture and education to name a few—this is a time of unprecedented access to large volumes of information about our modern day lives.

The great quantity and quality of information that we have at our disposal has the potential to allow us to glean insights about the state of the world in which we live and the demands for the future. Public and private sectors and civil society are tapping into these data to design, monitor and evaluate policies. Increasingly, environmental, economic and social data are being leveraged for decision making and companies which provide services to aggregate and analyze these data are at the forefront of a data revolution for sustainable development.

As the world decides upon the indicators to measure progress towards the Sustainable Development Goals, there is an urgent need to mobilize data to hold governments and decision-makers accountable. With dire constraints to finite natural resources, rising populations and levels of inequality, how can we tap into big data to track progress and modify the status quo? What kinds of data and analytical tools will be relevant to the 2030 Sustainable Development Agenda? What trends and practices may be useful and what are the greatest foreseeable obstacles which lie ahead?

Panelists:

Co-Founder, Executive Vice President, Governance & Accountability Institute;
Principal Data Scientist, Booz Allen Hamilton, PhD Astrophysicist;
Data Analyst Center for Sustainable Development, Columbia University;

Moderator:

Professor, NYU Center for Data Science; Editor-in-Chief, Big Data Journal