Data Helps Better the World at Bloomberg’s Sixth Annual Data for Good Exchange Conference

The UN articulated 17 Sustainable Development Goals (SDGs) to end poverty, fight all forms of inequality, and tackle climate change, while ensuring no one is left behind. Measuring each nation’s progress toward the SDGs requires an incredible amount of data. At the sixth-annual Data for Good Exchange (DG4X) conference held at Bloomberg’s Global Headquarters in New York City on Sunday, September 15, 2019, more than 500 data scientists, academics, policy-makers, representatives from non-profits and NGOs, and social good advocates came together to discuss the role of data science in achieving the SDGs.

“The SDGs are an ambitious, politically-agreed vision for what the world could be,” said Claire Melamed, Chief Executive Officer of the Global Partnership for Sustainable Development Data during the D4GX keynote. “How we get there is, of course, the question.”

There are 232 SDG indicators compiled from an unprecedented amount of country data that monitors progress. Many indicators are currently measured with outdated data or projections, especially when countries don’t have data to track annual progress. Currently, about 7% of the world’s population is excluded from the data, making these underserved populations invisible to government decision-makers.

“We need to ensure that every country has the data they need to inform the measures, to actually implement the agenda at the local level,” said Francesca Perucci, Chief of the Statistical Services Branch at the United Nations Statistics Division (UNSD), Department of Economic and Social Affairs (DESA). “So it’s a lot more than just tracking progress with the 232 indicators – it’s actually ensuring that everybody is counted in and has access to the data.”

This year’s conference centered around three content tracks that align to the UN’s 2030 Agenda for Sustainable Development: People, Prosperity & Peace, and Planet.


By first understanding the data needs related to an issue, advocates can then effectively analyze and leverage data to enact policies that end inequality of all forms. However, at present, 60% of the indicators are missing for many countries. This means that much of the policy work that’s done is based on guesswork rather than actual data.

Reliable data that portrays a fuller picture of people’s lives is key, but this requires eliminating data gaps and built-in bias around data collection. For example, birth registration data in some countries only records the father’s ethnicity. Algorithms based on this data will have an inherent bias towards the father. Any analysis based on this biased data could be wrong.

Users can recognize incomplete and biased data using data science tools. The work starts at the bottom of the statistical chain by addressing structural challenges related to linking statistical policies with national development policies. Many national statistical systems lack the technical and financial resources to work properly – ultimately creating opportunity for innovation. Algorithms can help accelerate decision-making, but they must be trained using quality data and purposeful design and application.

The People track also included a workshop on establishing responsible and ethical data for social good practices, which was hosted by Mastercard and The GovLab at NYU (read more about the workshop and its findings).

Prosperity & Peace

Civil society, the UN, the private sector, and governments work together to promote the rule of law and access to justice. On the other hand, communities build cities. Both require a bottom-up approach to the data.

About 55% of the global population resides in urban areas. In some areas, life expectancy can differ by as much as 20 years within five miles, as do opportunities for a better life. Since communities vary dramatically, this work is granular and requires individuals to be interviewed in order to diagnose complex problems and provide color about the communities.

Data – rather than intuition – often shows policymakers which programs are working and when to try fresh ideas. What Works Cities, a Bloomberg Philanthropies’ initiative, has worked with more than 150 U.S. cities to imbue data and evidence into the decision-making process to help mayors, city managers, and city councils implement policies to solve problems.

Communities can also work with larger cities and corporations to drive change. Two examples include the Mastercard Center for Inclusive Growth, which focuses on income inequality and unequal access to information faced by communities, and Accelerator for America, which enables mayors to share best practices, help enact change from the bottom up.

The Prosperity & Peace track also included two workshops: one about administrative data use led by Development Gateway, and a second about the use of algorithms to create a regional electrification plan led by CrossCompute.


Today, about 95% of the world’s fishing vessels are unmonitored, so there is little to no data about their locations and catches. Traditional monitoring tracks one location an hour, but solar powered autonomous boat trackers bridge this data gap by collecting 600 data points per hour. This creates opportunities to use data analytics and machine learning to deepen an understanding of what people are doing on the water.

This kind of data also informs philanthropic grants focused on cultivating the sustainable seafood industry through fish stock assessments. Creating sustainable fisheries positively effects species and the environment, and it also helps end hunger and poverty in underserved communities. It can also create opportunities for gender equality through micro-entrepreneurism for women and underserved youth in the downstream supply chain.

Ocean water also presents broader problems. As sea levels change, so does the probability of flooding. Climate Central’s Surging Seas Risk Finder uses NOAA forecasts to illustrate sea level rise and storm scenarios, demonstrating how a locale’s risk for flooding has already changed and may change further in the future.

Changing landscapes also affect wildlife. Ecoacoustic monitoring using autonomous audio recording units illustrate how the changing climate and decreasing snow and ice cover – combined with oil and gas infrastructure – impact wildlife migratory patterns in the Arctic National Wildlife Refuge.

On a more local level, the New York Climate Change Science Clearinghouse provides resources, including datasets, data products, maps and webinars that local government officials use to conduct climate vulnerability assessments. Rising temperatures, changes in precipitation, extreme weather events, and sea level rise all affect freshwater fish, foliage and land animals.

The Planet track also included two workshops. Scientists from the Wildlife Conservation Society (WCS) and World Wildlife Fund led one workshop focused on the emerging open source MERMAID toolkit and how data science can be used to save coral reefs. A second workshop facilitated by the Sustainable Development Solutions Network’s Thematic Research Network on Data and Statistics (SDSN TReNDS) – in collaboration with UN Environment – looked at how a digital ecosystem for the planet can help close environmental data gaps.

Visit our playlist on YouTube to watch the Data for Good Exchange 2019 keynotes, panels, papers, and immersion presentations: