The Center for Data Innovation spoke with Claire Melamed, executive director of the Global Partnership for Sustainable Development Data, a partnership devoted to achieving the United Nations Sustainable Development Goals. Melamed discussed the value of creating multi-stakeholder data ecosystems for development and the limited utility of some poverty reporting practices.
Joshua New: What is the Global Partnership for Sustainable Development Data, and what are its goals?
Claire Melamed: The Global Partnership for Sustainable Development Data is a growing network of more than 300 partners working around the world, including governments, businesses, civil society, international organizations, academia, foundations, and statistics agencies. Our goal is to use data to achieve the Sustainable Development Goals, and in doing so improving lives by fighting inequality, alleviating poverty, and promoting environmental sustainability.
We sum all that up as better data for better decisions and better lives. We achieve it by working with breadth and depth—in the broad partnership we convene partners, problem solving using targeted data collaboratives such as our ones on the environment, interoperability, and leaving no one behind. We can bring together a unique group of collaborators, such as governments, the private sector and non-governmental organizations (NGOs) as well as United Nations agencies and development banks, to solve problems together that no one group could manage on their own.
The depth comes through our work with eight countries—Colombia, Costa Rica, Ghana, Kenya, Philippines, Senegal, Sierra Leone, and Tanzania—that we collaborate with in a more focused way at a national level supporting their efforts to build more robust multi-stakeholder data ecosystems at the national and sub-national level. Check out our recent announcement of an African Regional Data Cube as a great example of the power of these partnerships. It’s a tool that harnesses the latest earth observation and satellite technology to help Kenya, Senegal, Sierra Leone, Ghana, and Tanzania address food security as well as issues relating to agriculture, deforestation, and water access. We’re working on it with NASA and the European Space Agency, as well as Amazon Web Services and Strathmore University in Kenya. It’s an example of how much more powerful data can be when different groups work together.
New: What is the Data4SDGs Toolbox, and why is this kind of resource so valuable?
Melamed: The Global Partnership for Sustainable Development Data was launched in 2015, alongside a new global framework called the Sustainable Development Goals (SDGs), adopted by world leaders during the 70th session of the United Nations General Assembly. There are 17 Goals, 232 indicators, and that both assumes a much more granular level of data collection, but also requires data innovation to achieve them. The SDGs are like a ‘to do list’ for the world—the things that leaders have decided are the most important for humanity, and for the planet.
Data, and the new possibilities to see the world in data that are now available thanks to new technologies, is an important tool for achieving the SDGs. To get the benefits we know are there means investing in the technology and people to use data equitably and effectively, and governments committing to use data in decision-making. It also means making sure people are protected from abuses of their personal data and have the data and the skills they need to hold governments to account; and that companies have the data they need to make investments that promote sustainable development; and that rules and infrastructures that govern data sharing and use around the world are fit for purpose, facilitating safe sharing of timely data going where it is needed, when it is needed. All these are part of the vision of the Global Partnership—for a world where data is powering progress for people and the planet.
But we know that there’s a lot of work to do to achieve that vision, and that’s where the toolbox comes in. The Data4SDGs Toolbox is a collection of resources produced by partners to help organisations achieve that vision, essentially practical guides developed by 15, and counting, partners on areas such as: gender data and the SDGs, using earth observation data for SDG indicators and making use of citizen-generated data. It’s a way of building a body of knowledge among our community.
New: The Partnership runs something called the Leave No One Behind Data Collaborative. What is the goal of this initiative? How can data help ensure nobody gets “left behind”?
Melamed: When world leaders adopted the Sustainable Development Goals, they made a historic commitment to build a world of peace and prosperity for people and for the planet, with a particular focus on leaving no one behind. In simple terms that means all human lives have equal merit and everyone deserves to grow up in a safe environment, with drinking clean water, having enough to eat, having access to education, given opportunities to progress, regardless of gender, race, or any disabilities. Those goals are simple, but achieving them is complex and asymmetry of information about vulnerable populations can reinforce disadvantage.
Who knows how many children on the shores of Lake Chad survive past their fifth birthday, or how many women died in childbirth in the middle of Mali, or how many elderly people in Somalia are malnourished? No one knows, and for those uncounted people, invisibility means powerlessness. If the poorest are not in the data, they are not in decisions that lead to change—and the goal of our work on “leave no one behind” is to put everyone in the picture, so that everyone’s lives have equal weight in the data.
Technology helps here: it’s now possible to bring together data from satellites, from surveys, from censuses, from mobile phones, and from other sources to show where and how people live. Data can now expose the reality of poverty around the world in a highly granular, multilayered way, leading to better understanding and better actions.
Our Leave No One Behind data collaborative is about promoting initiatives to help governments, NGOs, researchers and others collect, share and analyse more granular data, and use it to assess need, develop solutions, direct resources, and measure impact.
There are two parts to this. One is about persuading politicians to invest in more comprehensive data and the other is a technical dimension that is about finding ways to collect and analyze data more effectively, particularly about small and marginalised groups within a population so that it’s easier to know where progress is and is not happening. In the collaborative, our partners are doing both of these.
It’s not just about data that describes people’s lives—data also allows groups that are left behind to speak for themselves. We are also promoting wider use of what’s called citizen-generated data—data that’s produced directly by people or their organizations to monitor, demand, or drive change on issues that affect them. This can be done through community engagement, crowdsourcing mechanisms or citizen reporting initiatives, often organized and managed by civil society groups.
At our Data for Development Festival in Bristol in March, our partners came with some amazing stories about how communities have used data to press for change. One example was the Open Institute in Kenya, working with the Lanet Umoja community to collect data on what they most needed, and using it to persuade the government to build a new health clinic. It’s another way to prevent marginalization and “out of sight, out of mind” mentalities.
New: In 2015, you wrote an article expressing frustration how existing methods for reporting on global poverty did not provide enough meaningful data to do anything about it. Can you explain what you meant by this? Has this changed?
Melamed: The problem I was highlighting was that a focus on data for reporting on global poverty numbers—the total number or percentage of people living below the global poverty line in each country—is not that useful for countries that are trying to make policies to end it. An average doesn’t tell you the extent to which poor people are more likely to be women, or from a particular ethnic group, or living in a particular region. But these are all thing things you need to know to actually do something about it. That’s why the word “disaggregation” keeps coming up—it means separating out data on different groups so you can look beyond the average and know what to do.
I think there’s a lot more understanding that this is necessary than when I wrote that article, nearly three years ago. A lot of this has been driven by governments really deciding to invest in this area. In Ghana for example, a country we work with closely, the government is putting statisticians in every region, to get the data that will provide the kind of granular detail that will allow them to then put that data to work to end poverty.