Abstract

The global data ecosystem is rapidly evolving, presenting new opportunities and challenges for national statistical offices, international agencies, and other data producers. As countries work to safeguard the trustworthiness, impartiality, and quality of official statistics, the Fundamental Principles of Official Statistics (FPOS) remain the cornerstone of ethical, professional, and credible statistical systems. The Friends of Official Statistics Network– a global community of former senior statisticians, academics, experts, and advocates – can play a unique and increasingly important role in advancing these principles through advocacy, support, and advice, contributing to stronger national statistical systems and evidence-based decision-making across society.

Global data governance is entering a period of profound transition, seeking to go beyond a narrow focus on data protection and compliance toward enabling responsible, legitimate, and socially valuable data re-use  at scale. This shift re-frames purpose from minimizing risk to actively supporting public interest outcomes; anchors principles in Digital Self-Determination, emphasizing agency, dignity, and collective choice over how data are used; and introduces new processes, such as social license and participatory governance, to complement consent and legal authorization. Central to this evolution will be investing in new professionals - i.e. strategic data stewards, who can act as intermediaries translating values into operational decisions across complex ecosystems. In practice, this governance turn is increasingly supported by privacy-enhancing technologies that allow analysis while reducing exposure and misuse. Yet these advances unfold amid growing tensions: the acceleration of AI systems that concentrate data power, competing claims of digital sovereignty, and a deepening “data winter” in which access to data for science and public interest is shrinking despite unprecedented data abundance. The challenge ahead is to reconcile innovation with legitimacy -- building governance frameworks that enable data to be re-used, not just protected, in ways that are trusted, participatory, and fit for an AI.