This week the second UN Data Forum kicks off in Dubai with officials and experts highlighting the roles that data can play in promoting sustainable development. A 27-year-old UAE Minister of State for Artificial Intelligence will participate in the opening plenary, a small indication of the new and interesting ways in which data and its use might play out in the public policies of the future. But as conference participants discuss interoperability, disaggregation, innovations and collaboration, it is worth revisiting what’s happened since the 2013 High Level Panel and others called for a “data revolution” that would generate real-time availability of more accurate data, and thus more awareness and action on development progress.
The big picture is that although much progress has been made on the reduction of extreme poverty and some measures of health, country efforts on the SDGs since 2015 are off-track. Not one G20 country is on track to achieve all the SDGs, according to Bertelsmann Stiftung and SDSN. My colleagues’ projections show that 44 countries with populations of more than one million people will fail to meet the SDG target on secondary education, 12 will fail to meet the electricity access goal, and 14 the sanitation goal. Of 25 SDG indicators assessed in a recent paper by Homi Kharas and co-authors, only five are on track to make 50 percent of the progress needed by 2030 (child mortality, hepatitis B, malaria, access to electricity, and extreme poverty), and most are significantly off track (two, childhood obesity and air pollution, are even regressing). Much of the pending agenda from the Millenium Development Goal-era is also stagnating according to the 2017 Sustainable Development Goals Report.
The SDGs are aspirational, meant to inspire effort and not to prescribe specific solutions. At three years out and with some commentators already pessimistic about the realism of the 15-year horizon for the goals, the key global and domestic strategy is to achieve as much sustainable development as possible given economic and other constraints.
So how well do the multitude of data initiatives unleashed around the SDG serve this purpose? And what could be done to accelerate efforts?
More data is being produced, but completeness, accuracy and availability remains a challenge.
In 2015, when the SDGs were agreed, the availability of data for tracking progress towards the goals and targets was not a factor that was taken into consideration. For many SDGs, data was simply not available. In 2015, of the 232 indicators, only 42 percent were Tier 1, meaning that they had an established methodology and regularly accessible data, and only 62 percent of Tier 1 indicators (25 percent of all indicators) could be found online in a publicly accessible format. Now, three years later, fewer indicators (88) lack available data and 55 have some form of method but no data. The UN asserts that data quality and availability has improved in recent years, and in the big data world, an industry group crows that 90 percent of the world’s data was produced over a recent two-year period (2011–2013).
This is good news, but gaps remain in the basic building blocks of data that underpin the indicators.
Investing in data systems is costly, but funding for low-income countries is still miniscule.
Building and sustaining data systems comes at a significant financial cost for countries, particularly low-income countries. The estimated cost for countries to produce data for the tier 1 and tier 2 SDG indicators is $2.8–3 billion USD per year, and, as of 2017, there was a funding gap of 685 million annually. While efficiency gains with new sources and technologies are promising, there is still the hard work of capacity building and generating the data basics that are relatively unsupported.
Political economy of data still needs attention.
Some countries may under or over report on statistics to influence aid allocation. Others have fiscal incentives in place to over- or under-report numbers of students or agricultural yields. A new law in Tanzania outlaws questioning of official data and statistics, while in the US, budget priority changes threaten sources of data used by scientists to track climate.
Value of data for better policy: still mostly unknown.
At the heart of the calls for the data revolution was a theory of change that posited that more and better data would lead to more effective policies and programs. “Better data, better decisions, better lives” is the tagline of the Global Partnership for Sustainable Development Data (GPSDD) that embodies this belief, shared by all of us working on data and evidence for policy. GPSDD has put together some useful case studies on the value of data, and there are some efforts to assess the value of information in high-income countries as in the UK, but given the paucity of investment in country data systems, there is still something missing.
Some overkill in SDG tracking at the global level?
One area that has received attention and funding is SDG tracking. September’s UNGA week featured at least four SDG trackers from the Global Partnership for Sustainable Development Data, Our World In Data, the Bill & Melinda Gates Foundation Goalkeepers, and the SDG Index and Dashboard. A next step will be to assess whether the global tracking—given the deficiencies in country data—are paying off with more policy effort on the SDGs.
For every limitation noted, there also signs of momentum and interest in delivering on the promise of a data revolution. As part of Ghana’s data roadmap process, the government has begun a national ID card roll-out which will increase access to education, healthcare, and employment. Sierra Leone is also investing in a data roadmap which will “facilitate SDG planning, implementation, monitoring, evaluation and reporting.” Kenya has created an Intergovernmental Network on Open Data for Agriculture and Nutrition, and Tanzania is linking global and regional indicators with national development indicators using an advanced data planning tool “to adapt their data production to the priority data needs from policy makers.”
But there is more work to do. And to make real progress, supporters of the data revolution need to keep their eye on the ball—less planning and summits perhaps—and more attention to quantitative progress in the completeness, accuracy, and availability of data.