Better data = better lives. This is now a formula known and pronounced by many, which undergirds the 2030 Agenda for Sustainable Development. For the United Nations Department of Social and Economic Affairs, collecting data “about the world and the people who live in it” is essential to assessing “what it takes to realize a better world for all.” More than just a technical matter though, concentrating data in the hands of the United Nations (UN) and wielding indicators as “technologies of global governance” raise a number of questions, some new and others old. If, as in the words of the Director of the UN Statistics Division Stefan Schweinfest, “what gets measured is what gets done,” what is to come of global problems that go unmeasured or are unmeasurable? With states now being assessed based on the performance of their statistical systems, as best illustrated by the World Bank’s Statistical Performance Indicators (SPI), to what extent is the push for internationally comparable data in the context of the 2030 Agenda generative of inter-state hierarchies? Moreover, is data for measuring the Sustainable Development Goals (SDGs) being collected, managed, used, and diffused in a fair, transparent, just, and respectful way so as to prevent the perpetuation of global inequalities and harm?

In November 2022, the Geneva Graduate Institute’s Global Governance Centre convened a roundtable on the data practices, challenges, and futures of measuring the sustainable development goals (SDGs). Organized in partnership with the SDG Lab and Deloitte Switzerland, the roundtable brought a number of key representatives from international organizations, academia, civil society, and the private sector to reflect on the practices and politics of SDG data from a cross-sectoral, multidisciplinary perspective:

• Steve MacFeely, Director of Data and Analytics at the World Health Organization (WHO)
• Bojan Nastav, Acting Chief of Statistical Analysis at the United Nations Conference on Trade and Development (UNCTAD)
• Moira Faul, Executive Director and Senior Research Fellow at Network for International Policies and Cooperation in Education and Training (NORRAG)
• Kate Richards, Advocacy Manager of the Global Partnership for Sustainable Development Data
• Vincenzo Chiochia, Director and Head of AI Insights and Engagement at Deloitte Switzerland

With the aim of consolidating a multistakeholder forum in International Geneva, the Measuring the SDGs roundtable was the first of a series of data-focused events that will take place in the year ahead.

Retracing the UN’s turn to indicators as a means of knowing and governing global problems, panelists highlighted a range of complications that have emerged in translating the seventeen SDGs into measurable indicators, with a focus on lacking statistical capacity and data incompleteness. In addition, pointing to embeddedness of data in power relations, they provided insights into the exclusionary dynamics of governing through data, suggesting ways of enhancing the data capacities not just of states and international organizations, but also of less powerful actors. Taking the pulse of the current SDG data system, panelists carved out pathways for ensuring a fairer and more just data future.

Lacking Statistical Capacity and Data Incompleteness

In spite of the UN’s demands, most of the world’s countries lack the capacity to actually collect and communicate data on SDG indicators, thereby pointing to significant data gaps and weak statistical capacity. Ambitious in its goals, the SDG global indicator framework represents an “unprecedented statistical challenge,” as underscored already back in 2016 by Mogens Lykketoft, who served as the President of the seventieth session of the UN General Assembly. Far more complex and four and a half times more expansive than the Millennium Development Goals (MDGs), many of the 232 SDG indicators are entirely new, meaning that states do not have readily available data. According to some recent estimates on SDG reporting, no single country has data covering ninety percent of the SDG indicators, with most countries only having data on roughly fifty percent of the indicators. As many states around the world are struggling to maintain their existing national statistical systems, the SDGs only put greater strain on National Statistical Offices.

Definitional Ambiguity

Alongside data incompleteness and weak statistical capacity, definitional ambiguity equally stands in the way of tracking and monitoring SDG progress. Indicative of such discrepancies, the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) devised a three-tier classification system to distinguish between SDG indicators based on “their level of methodological development and the availability of data at the global level.” To qualify as Tier 1, indicators must be clearly conceptualized, have an internationally agreed upon standard for data collection, and already be measured by at least fifty percent of concerned countries. Tier 2 indicators have been defined and may potentially be but have not yet been measured based on a shared methodology. Tier 3 indicators, in contrast, remain conceptually ambiguous and lack an accepted data collection methodology. Even in success stories, orchestrating definitional and methodological alignment at the international level is a costly endeavor, which often excludes less powerful actors from data deliberations and produce policy blind spots.

Being Marginalized and Invisibilised by Data

Data can empower but can also exclude and harm. For one, communities are frequently solicited to give up data on themselves to powerful institutions, but have no control over how and to what ends their data is utilized, how their data is then governed after being collected or how they are represented when this data is assembled and interpreted. Secondly, the widespread lack of disaggregated data based on age, sex, socio-economic status, or other categories invisibilizes the needs, experiences, and priorities of vulnerable groups. The overreliance on aggregated data then complicates the task of leaving no one behind as “vulnerable segments of the population remain hidden in the data.”

A reflection of the UN’s call for a data revolution, the 2030 Agenda for Sustainable Development has placed data at the forefront of managing global problems. According to the UN Statistical Division, in order to “fully implement and monitor progress on SDGs, decision makers need data and statistics.” At the heart of data-driven governance arrangements is the belief that data may solve collective inaction by subtly monitoring and putting pressure on states to ensure they fulfill their political commitments. While the promises of data are indeed enticing, technical initiatives designed with the intention of boosting the data capacities of states and international organizations, be it through surveys or big data, are inherently embedded in and reflective of power dynamics. Data is thus not only a force for good but also equally one that orders global society, excludes, and potentially harms, thereby necessitating deeper reflection on the politics of quantification and data-driven governance.

Image by Pete Linforth on Pixabay