Higher education runs on sprawling edtech stacks built on proprietary, opaque data models — fragmentation that deepens vendor lock-in, drives up costs, and blocks institutions from building the trustworthy, AI-ready tools their students need.

At DataKind, we've spent years building the alternative: open source infrastructure and products now used by 100+ broad-access institutions serving over 1 million students. This talk shares how we use Apache Spark, Apache Airflow, and Apache ECharts to power two freely available, open core education products designed to support student graduation success.

Education is where this panel starts, but not where it ends. Our work in education has sharpened our view on an open source data infrastructure approach — one that turns fragmented institutional data into a reusable foundation that can unlock impact across student success support, institutional technology transformation, and sector AI innovation.

We will share our work on a deployable open data model and associated infrastructure for staging, mapping, transformation, and access, built around four pillars of trust (modular pipelines for institutional autonomy, public versioned code, standardized data dictionaries, and schema lineage tracking) and discuss practical challenges — including data quality, system interoperability, and explainability — to translate open infrastructure into tools that non-technical teams can actually use.

Attendees will leave with access to open source public-good infrastructure for student success; frameworks for evaluating shared data models and AI-ready infrastructure; practical approaches to institutional deployment; and opportunities to join a community building an interoperable foundation on which an ecosystem — not a single organization — can innovate.