Strategic Data Project Convening 2026

Fuel up for the day ahead! Join us for a light breakfast and Leadership Stories from the Cohort prior to the kickoff of the workshop.

This session is closed to Cohort 17 fellows only. 

This is your chance to connect with peers, grab a coffee, and get energized for an exciting day of learning and collaboration.

Session Description: Reviewing degree programs or other similar administrative tasks often surfaces data that is difficult to discuss without damaging trust or relationships. This session will share a method for aligning shared program goals with accessible, simplified data to support constructive, relationship-centered conversations about program performance and improvement using research-based communication techniques. This session is designed for institutional researchers, academic leaders, and faculty involved in program review or academic planning.

Intended Audience: Institutions of higher education; Data analysts or independent researchers; Senior leaders (including cabinet-level leaders and executive directors)

Session Description: Data initiatives often fail at the adoption stage due to user resistance and data grief. This structured exchange will convene leaders, analysts, and directors to crowd-source solutions for managing data change. Participants will rotate through themed round tables on easing emotional resistance, aligning system design to user workloads, and building trust between data builders and decision-makers.

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

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)