Putting Data to Work for Real-World SDG Progress
Timely, high-quality data is critical to reaching the United Nations Sustainable Development Goals — but harnessing it requires new ways of thinking, collaboration, and investment in national statistics systems.
*This piece originally appeared in Nature.
As governments, businesses and researchers collaborate and share data, they drive progress toward the UN Sustainable Development Goals. Credit: skegbydave/iStock/Getty Images
In the early days of the COVID-19 pandemic, Tom Moultrie worried about whether South Africa’s national statistics system was prepared. To combat a fast-spreading disease, health officials needed accurate and timely data on patient deaths. The nation had a well-developed civil registration and vital statistics (CRVS) system, which tracks births, deaths, and marriages, but Moultrie—a demographer, director of the Centre for Actuarial Research at the University of Cape Town, and expert member of the international research group, the Sustainable Development Solutions Network's Thematic Research Network on Data and Statistics (SDSN TReNDS) — knew the system had a weak link.
Because of the stigma associated with HIV/AIDS, which emerged in South Africa in the mid-1980s and remains a major cause of death, the country kept cause-of-death data confidential for several years when people died from any disease. But this was much too slow to provide actionable intelligence in a pandemic. Early in 2020, Moultrie and his colleagues at the South African Medical Research Council scrambled for a solution. Ultimately they repurposed the country’s mortality surveillance system to track mortality on a weekly basis, and then used that data to calculate excess deaths to help estimate the scope of the country’s COVID-19 outbreak.
Combating a pandemic requires timely and quality data at every scale. Just as a doctor needs quick test results, public-health officials need to know how to allocate scarce resources, and when and where to enforce public-health measures. Yet, COVID-19 has exposed acute weaknesses in data systems worldwide, in even the wealthiest nations. Shortages of tests and medical equipment were compounded by conflicting and incomplete data. And in countries like the United States, India and others, data problems have become political ammunition, causing public uncertainty and mistrust.
Good data is essential for addressing public health crises as well as the 17 Sustainable Development Goals (SDGs) that the United Nations is tackling in its ambitious 2030 Agenda for Sustainable Development, says Jessica Espey, director of SDSN TReNDS. In September 2020, with the world already five years behind its ambitious targets, UN Secretary-General António Guterres called for a “decade of action” to deliver the SDGs. So far, the lack of quality, timely data has emerged as the biggest hurdle, Espey argued in Nature last year.
What’s most confounding, however, is that the world already has much of the data it needs, as South Africa’s retooling of mortality data demonstrates — and much of what’s missing can be obtained. Data collected by national statistical offices (NSOs), including census data and CRVS, can now be complemented by data from Earth observations, mobile phones, social media networks, and other sources. Emerging tools in big data analytics, such as artificial intelligence and improved data visualization, offer previously elusive insights.
But this explosion in data has far outstripped the capabilities of many NSOs to handle it, and stepping up those capabilities is key to achieving the SDGs, Espey says. The world invests just half of the US$1.3 billion per year needed to achieve full SDG monitoring, according to a 2019 report from The Partnership in Statistics for Development in the 21st Century (PARIS21), an international organization that promotes better statistics throughout the developing world.
New data solutions and new thinking are needed around the 2030 Agenda and post-pandemic recovery efforts, and this week thousands of data experts are gathering online for the World Data Forum to discuss them. Ultimately, smart strategies are essential. That means enhancing traditional data tools and methods, while capitalizing on emerging data sources and tools, tapping data expertise, and finding new ways to collaborate across sectors.
Strengthening traditional data
When Paula Caballero, a Colombian official, proposed the first draft of the SDGs in 2012, she imagined an agenda of collective responsibility and empowerment “to catalyze a sense of shared destiny.” In 2015 the global SDG summit delivered, with its bold declaration to “leave no one behind.”
Leaving no one behind requires well-functioning CRVS systems. These are key to monitoring 12 of the 17 SDGs, according to a report by SDSN TReNDS and the nongovernmental organization Open Data Watch. By properly recording births, deaths and marriages, countries can better monitor important statistics, such as maternal and infant mortality and deaths from disease.
A functional CRVS system can also have a tremendous impact on gender equity, a cross-cutting SDG goal, by facilitating education, access to social services, and preventing child marriage. “These need to be incorporated into the foundations of the country's information system — no quick fixes, no Band-Aids, no duct tape,” says Shaida Badiee, who co-chairs SDSN TReNDS and advocates for an open digital ecosystem from her non-profit Open Data Watch.
A good census is also required, but in many countries, they’re often infrequent and may not count people in remote communities or conflict zones. Language barriers can also stymie counting, as can resistance to participation. “The census is crucial, but it’s sufficiently problematic in most of the developing world that one can't rely on it alone for quality data,” says Moultrie.
For that reason, nations have begun augmenting census data with geospatial data and other data sources to create gridded population datasets, which can provide a better understanding of where groups of people are located. Such data help count people who need aid amid natural disasters, and more accurately estimate disease spread, poverty, and pollution exposure, according to a recent SDSN TReNDS report.
To protect vulnerable citizens from dangerous pollution levels, Los Angeles integrates data from sensors, NASA satellites, and GPS-enabled asthma inhalers. Credit: Westend61/Getty Images
Harnessing new data
Tapping new sources of data, both analog and digital, are essential as well. In New Zealand, an NGO called Sustainable Coastlines uses citizen science to monitor the haul from beach cleanups they conduct to fight the marine plastics problem. But when they tried to use the data to lobby for new laws and better business practices, companies and politicians wanted more rigorous evidence. “The credibility of the methodology wasn’t there,” says Camden Howitt, the group’s co-founder.
Efforts are underway to make citizen science more robust. Jillian Campbell, head of monitoring, review, and reporting at the UN Convention on Biological Diversity, and an expert member of SDSN TReNDS, is working to bolster the use of citizen science in official SDG monitoring. She and colleagues from her previous role at the UN Environment Programme (UNEP), IBM, the Wilson Center, and others compiled and harmonized data collected by citizen science groups worldwide to formulate new reporting standards on marine litter to strengthen data collection. IBM contributed machine-learning tools and built a model to predict future coastal plastic density. This new data source should help inform and improve future cleanups.
Data from emerging technologies play key roles as well. Los Angeles, for example, is tackling its air pollution problem by harnessing data from sensors around the region that gauge ozone and particulate matter levels, says Jeanne Holm, LA’s Chief Data Officer and another member of SDSN TReNDS. Other data from GPS-enabled asthma inhalers help pinpoint problem areas for vulnerable populations. Meanwhile, the city is working with NASA to combine satellite and sensor data and then use artificial intelligence to predict upcoming pollution levels, Holm says.
Public-Private Partnerships
Much of the world’s data and analytical tools lie in the private sector, and to make the best of them, corporate engagement and collaboration are essential. That can mean in-kind donations, like the US$200,000 a year of cloud computing that Google committed to provide through 2030 to support Freshwater Ecosystems Explorer. This desktop tool, which Jillian Campbell helped develop at UNEP, integrates remote sensing data, and layers in water quality, ecosystem, and population data to help support SDG target 6.6 — halting degradation of freshwater ecosystems. “This allows us to do monitoring that was not possible even a few years ago,” Campbell says.
Yet sharing data across sectors can require delicate negotiation, which can often be the difference between success and failure. In Colombia, SDSN TReNDS and a regional think-tank, CEPEI, brokered a partnership between the National Administrative Department of Statistics (DANE) and the Bogotá Chamber of Commerce (BCoC) to enhance SDG reporting. BCoC ran a registry with 2.6 million reports from companies.
After a year-long negotiation, much of it focused on protecting privacy and proprietary information, DANE obtained data on manufacturing and access to banking, which relate to SDGs 8 (decent work and economic growth) and 9 (industry, innovation, and infrastructure). Meanwhile, the BCoC gained expert insight that enabled them to extract valuable new information.
Of all the work that went into the Bogotá data reconciliation project, the legal work was the most intensive, says Fredy Rodríguez, CEPEI’s data coordinator. The experience encouraged SDSN TReNDS to create the Contracts for Data Collaboration initiative, which features a growing collection of data-sharing templates to help overcome legal barriers and streamline cross-sector data collaborations.
Stepping up support
With the deadline for achieving the SDGs just a decade away, both political will and new investments are required, Badiee says. NSOs are often weak and underfunded, particularly in the Global South, and the COVID-19 pandemic has exacerbated these weaknesses, according to a June 2020 survey by the UN and World Bank. “Countries need to invest in better statistics and better capacity for managing data,” she says. Such investments pay off. For example, the Philippines modernized its national statistics office in 2013, and it expects to save US$6.09 billion over five years, according to a 2018 TReNDS report.
Ultimately, thriving national data systems with empowered chief statisticians are essential, a SDSN TReNDS report argues. Beyond that, we need a dynamic new ecosystem of development data that pulls from all sectors and spurs everyone from governments and corporations to citizens to collaborate. This new ecosystem is taking shape, Espey says, and it will drive progress and forge new and efficient paths toward sustainability.