Strategic Data Project Convening 2026

Session Description: Education data systems are abundant yet fragmented, unstable, and difficult to reuse for
strategic decision-making. This session offers two solutions to address these challenges. First, we present a FAIR-aligned framework and practical tools for mapping, stabilizing and reusing education data from a cross-state study of 3,822 public K-12 datasets. Then we present findings from an empirical study of AI-produced mappings across higher education data sources to show which AI models provide the best balance between performance and cost. Together the session provides an interoperable way to work with K-12 datasets and a means to scale the mapping of education data.

Intended Audience: Districts, charters or other local education agencies; Managers of data or technology teams; Senior leaders (including cabinet-level leaders and executive directors)