Enacting indigenous data governance in official statistics: A rights-based framework for settler-colonial and comparative statistical systems

National statistical systems are central to how states recognise populations, define public issues, allocate resources, monitor inequities and report progress. For Indigenous Peoples, official statistics shape whether collective and individual rights are visible, actionable and accountable within public policy. Yet Indigenous Peoples remain invisible, misclassified, externally defined or deficit-framed in many official statistical systems. This article argues that identification, disaggregation and technical quality are necessary but insufficient foundations for statistics that uphold Indigenous rights. Disaggregation can support visibility, but visibility without Indigenous authority can reproduce surveillance, extraction, statistical misrecognition and harm. This article develops the Indigenous Statistics Governance Framework as a rights-based analytic framework for assessing how Indigenous Data Governance can be embedded within official statistical systems. The framework is grounded in international human rights instruments, Indigenous Data Sovereignty and Indigenous Data Governance scholarship, official statistics standards, data stewardship principles and practice examples drawn primarily from Canada, Australia, Aotearoa New Zealand and the United States. It identifies four interdependent system functions: Recognition, Stewardship, Authority and Accountability. Recognition concerns whether Indigenous Peoples are made visible in ways that are accurate, meaningful and governed. Stewardship concerns whether Indigenous data are cared for safely and responsibly across the statistical lifecycle. Authority concerns whether Indigenous Peoples have decision-making power over statistical priorities, concepts, classifications, interpretation, access and use. Accountability concerns whether statistics support obligations, implementation, equity, redress and institutional performance. The framework is offered as a transferable analytic framework, not a universal implementation model. Its wider application requires contextual adaptation, particularly in statistical systems where Indigenous Peoples are not legally recognised, where identity is captured through alternative classifications, or where Indigenous governance institutions are absent, emergent or contested. The article reframes official statistics as rights-enabling and rights-implicating infrastructure and provides a practical structure for assessing whether Indigenous Data Governance is embedded in statistical practice.