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.

Open source is often presented to mission-driven organizations as a technology choice. Our research suggests that durable adoption requires a different operating model; it requires both an investment in software creation, but also the necessary adoption infrastructure to sustain its use.
As DataKind expanded its own software portfolio, we studied implementation partnerships, governance arrangements, procurement structures, and business operations across four sector contexts. The pattern was consistent: technically mature open-source solutions fail to scale or persist when implementation, stewardship, and support capabilities remain external to the institutions responsible for long-term use.

We call the missing piece adoption infrastructure — the governance, implementation services, maintenance capacity, and partner ecosystems that turn open technology into durable public infrastructure. Proprietary vendors bake these functions into their business model. Open source ecosystems usually don't — leaving them fragmented, underfunded, or dependent on grant cycles that were never designed to sustain them.

This panel asks who should close that gap, and how. We'll examine the case for treating interoperability standards and shared IP as long-term public goods rather than one-off deliverables; why procurement processes built to buy licenses struggle to buy the implementation work adoption actually requires; and what it would mean for philanthropy specifically to fund infrastructure and stewardship rather than repeatedly funding new software.
Attendees will leave with a framework for diagnosing why open solutions stall after launch, and a set of priorities for funders, procurement teams, and adopting organizations who want their investments to actually last.