Data assets & lifecycle management

This layer assesses the breadth, quality and representativeness of a country's data assets across key domains, including language and culture, health, geospatial, environment and resources, economic activity, demographics, infrastructure, and public administration. It asks how well those assets reflect the nation’s diversity and support AI capability across both model development and in-life deployment. It evaluates stewardship across the full lifecycle: creation and sourcing aligned with Indigenous Data Sovereignty and ethical principles; preparation and curation to ensure accuracy, provenance and reuse; access and use governed by clear licensing and trusted cross-border arrangements; and long-term retention, deletion and auditability that uphold privacy and public trust. It examines whether datasets are machine-ready, regularly refreshed, well-documented and discoverable, and whether they sit in secure environments that enable responsible AI training, post-training alignment and in-life deployment. It also considers whether government and publicly funded data are made available in open, reusable formats while safeguarding sensitive datasets and respecting community rights, including supporting evaluation, monitoring and continuous improvement of AI systems.