Strategic Data Project Convening 2026

Session Description: Instructional coherence is the emerging practice of aligning all aspects of learning to reduce “instructional code-switching” between varying procedures, approaches, and curriculum. In this session, we will share learnings from a district pilot and randomized control trail on implementing instructional coherence for early literacy. Those interested in supporting student literacy, innovative models for supporting student learning interventions, and advancing how to leverage formal and informal student data to meet student needs should attend this session.

Intended Audience: Districts, charters or other local education agencies; State education agencies; Service providers

Session Description: Schools and districts are always searching for ways to streamline data collection and help generate proof points. And while the vast majority of organizations are using the Google Suite, most are using it for its basic functionality and not tapping into its full potential. This session will highlight various tools built in the Google Suite and inspire school/district leaders to think about how to leverage the Google Suite to improve their own orgs.

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

Session Description: Generative AI is reshaping the education data landscape, yet many professionals approach it without collaboration across the data community. This session brings together participants in similar roles and in cross-role conversations to discuss AI adoption and use, deepening shared understanding of successes, challenges, and needs. Whether you are new to AI or an expert, the session offers space to exchange experiences, learn from peers, and build connections that support effective and responsible AI use.

Intended Audience: Managers of data or technology teams; Data analysts or independent researchers; Senior leaders (including cabinet-level leaders and executive directors)

Session Description: Many data practitioners serve as the sole data voice within functional departments or small organizations where they must navigate cultures unfamiliar with rigorous evidence. This session brings these lone strategists together to swap strategies for establishing trust, managing workflows, and avoiding duplicative silos. Data strategists in LEAs/SEAs in HR, Finance, Safety, Federal Programs, etc.—and anyone serving as a "team of one"—are encouraged to attend and find their people.

Intended Audience: Data analysts or independent researchers; Districts, charters or other local education agencies; Philanthropies and Foundations

Session Description: This session demonstrates how to automate data pipelines to support advanced analysis and decision making. We highlight a predictive model from Metro Nashville Public Schools identifying 3rd-grade retention risks a year early to trigger proactive interventions and midyear implementation reports from AmplifyLearn.AI for a dozen public school districts on the implementation of the Colleague AI platform. This session will show how even small teams can create reports that dynamically update with new data.

Intended Audience: Data analysts or independent researchers; Managers of data or technology teams; Senior leaders (including cabinet-level leaders and executive directors)

Session Description: Knowing whether educational improvement efforts are yielding improved student outcomes often depends on information outside state longitudinal systems. From Logs to Learning shows how a data standard for tutoring creates shared definitions that enable transparency, comparability, faster feedback loops, and high-quality continuous improvement. We will share an open tutoring data standard and toolkit, then present a case study with Florida Tutoring Advantage, Littera, and Accelerate demonstrating earlier-cycle monitoring, cross-district benchmarking, and implementation refinement.

Intended Audience: State education agencies; Districts, charters or other local education agencies; Service providers

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)

Session Description: Fast-growing public-sector and nonprofit organizations often need data insights before their systems are ready. This session shares how our team developed a sprint-based Data Strategy Delivery System to deliver useful dashboards, tools, and data products while still supporting a long-term data strategy. Data practitioners and leaders, especially those working in low-maturity data environments, will leave with concrete approaches for turning fragmented data into actionable insight.

Intended Audience: Nonprofit organizations; Managers of data or technology teams; Data analysts or independent researchers