Better data governance for better SDG tracking
Nepal has made a visible commitment to the Sustainable Development Goals (SDGs) that it aims to achieve by 2030. National plans, annual reviews, sectoral dashboards, and development reports increasingly rely on data to track progress and inform policy decisions. Yet beneath this data-driven ambition lies a quieter but far more consequential problem. Nepal’s development policies are often shaped by data that is weakly governed, fragmented across institutions, and insufficiently accountable to those it represents. In this data-driven world, data governance is no longer a technical concern confined to statisticians or IT units, but it has become a central policy issue.
Without clear rules governing how data is collected, shared, protected, and used, even well-intentioned SDG policies risk being inefficient, inequitable, and disconnected from ground realities. The Organization for Economic Co-operation and Development (OECD) defines data governance as the legal, institutional, and ethical frameworks that determine who controls data, for what purpose, and with what accountability. For policymakers, this matters because data now underpins almost every major public decision to attain SDGs by 2030. It matters from health resource allocation to education planning and social protection targeting.
Nepal’s SDG reporting architecture relies heavily on indicators and administrative data systems. While this has improved monitoring, it has also created a false sense of precision. Angus Deaton, winner of the 2015 Nobel Prize in Economics, argues that poor-quality or poorly governed data can distort policy priorities rather than improve them. He exemplifies that the documentation of how people live and how much they spend, and on what, has long been used as a political tool to make visible the living conditions of the poor to those in power, to shock, and to agitate for reform. And this is a sole example of poor data governance.
Nepal has made some effort to govern the data system. In the health system, the Health Management Information System (HMIS) is the backbone of planning and reporting. Yet, problems of underreporting, inconsistent definitions, and limited verification at the facility level are still there. For instance, health workers, often overburdened, are required to report upwards to meet donor and national targets but rarely receive feedback on how that data shapes decisions.
The result is a system where data flows vertically, but accountability does not. Policymakers may see improving indicators, while frontline realities, such as medicine stock-outs, workforce shortages, or rising antimicrobial resistance, remain poorly captured. In such contexts, “data-driven policy” becomes more aspirational than accurate.
Similar challenges are visible in the education sector. Nepal has made notable progress in expanding school enrollment, particularly at the primary level. However, aggregate education data often masks deep inequalities across geography, gender, disability status, and socioeconomic background. Education Management Information System (EMIS) data struggles to adequately capture learning outcomes, dropout dynamics, or the lived experiences of children in remote and marginalized communities.
Children with disabilities, seasonal migrants, and those affected by climate-induced displacement are frequently missing from datasets altogether. Feminist and equity-focused data scholars have argued that what is not counted is often not prioritized. For policymakers, this has direct consequences. Budget allocations, teacher deployment, and school infrastructure investments are made based on incomplete pictures, undermining the SDG commitment to “leave no one behind.
Data abundance, governance scarcity
Nepal does not suffer from a lack of data; it suffers from weak data governance. Across health, education, and social protection, large volumes of data are generated through government systems, donor-funded programs, NGOs, and private technology platforms. Yet this expansion has occurred without adequate governance frameworks to regulate data ownership, interoperability, and accountability.
As a result, data systems remain fragmented across institutions, shaped more by reporting requirements than by policy needs. Scientific and policy literature warns that data accumulation without governance leads to inefficiency rather than insight. The World Bank has noted that in the absence of strong governance arrangements, data systems tend to multiply in silos, increasing costs while reducing their usefulness for decision-making.
The OECD also emphasizes that effective data governance requires clear mandates and coordination across public institutions, not ad hoc technological solutions. For Nepal, continued data abundance without governance risks undermines evidence-based policymaking rather than strengthening it.
Political economy of data
Data governance is inherently a political economy issue because data increasingly determines how resources are allocated, priorities are set, and policies are evaluated.
Decisions about what data is collected, which indicators are prioritized, and how information flows across institutions are shaped by incentives, mandates, and authority structures. These design choices influence whose realities are visible in policy debates and whose voices carry weight in decision-making. When data systems are fragmented or weakly governed, influence tends to concentrate where analytical capacity and control are strongest, often at central or aggregate levels. This does not require intent; it emerges from institutional design. As a result, data governance affects not only technical efficiency but also the distribution of power between national institutions, subnational governments, and communities.
Understanding data governance through a political economy lens is, therefore, essential for ensuring that evidence-informed policymaking remains inclusive, accountable, and aligned with public interest.
In Nepal, public data systems in sectors such as health and education have evolved through multiple programs and reporting requirements over time. While this has increased data availability, it has also resulted in fragmented platforms, parallel reporting structures, and limited interoperability.
Scholars describe such structural imbalances as a form of “data colonialism”, where data generated in low- and middle-income countries is aggregated and analysed through externally oriented systems, while national decision-making capacity remains uneven. When data increasingly informs policy, weak governance frameworks can unintentionally shift influence away from domestic institutions and communities, underscoring the need to treat data governance as a core public policy concern rather than a purely technical issue.
Tracking SDG promises
The Sustainable Development Goals (SDGs) are ultimately a promise to reduce inequality, improve well-being, and build resilient and accountable institutions. That promise cannot be fulfilled on the basis of poorly governed data. When data systems are fragmented, unaccountable, or detached from ground realities, policies risk reinforcing exclusion rather than addressing it.
For Nepal, strengthening data governance is not about keeping pace with global digital trends or adopting the latest technological solutions. It is about ensuring that development policies are informed by lived realities, that marginalized populations are visible within national data systems, and that public decisions are grounded in trust rather than assumption. Data must serve people, not just indicators. The future of Nepal’s SDG journey will not be determined by how much data is collected, but by how responsibly, equitably, and transparently that data is governed.
For Nepal to effectively track overall development and progress toward the SDGs, the country needs a data governance framework that is coherent, inclusive, and accountable. This means establishing clear legal mandates on data ownership, sharing, and protection across sectors; ensuring interoperability between national and subnational data systems; and embedding mechanisms for data verification and feedback from frontline service providers and communities.
Data governance must go beyond reporting compliance to prioritize data quality, equity, and public trust. Critically, marginalized populations must be systematically counted, and subnational governments must have both access to and authority over the data they generate. Without these foundations, data will continue to inform reports rather than decisions, and Nepal’s SDG monitoring will remain detached from lived realities.
