The majority of data integration efforts focus on health and welfare.
Education is not typically part of these initial efforts as it is seen as a more challenging sector. Only 54% of established integrated data systems fully include K-12 data.
Preventing silos is difficult.
Research efforts are often tied to a researcher, who does not have incentives to share his/her dataset due to the competitiveness of the field. We believe a focus on transparency and open source expectations from the outset will facilitate future collaboration and applications.
Education data work is challenging due to families' fears over privacy, data breaches, and clarity of purpose.
Maintaining a fully ground-up approach, including building demand within agencies/organizations and communities, will increase buy-in, privacy protection, and success of responsible initiatives.
New York University’s Administrative Data Research Facility
Creating a new approach for sharing data across agencies and states.
Urban Institute’s Center on Education Data and Policy
Making high-quality federal education data available for fast analysis.
Stanford Education Data Archive (SEDA)
Finding bright spots in existing education data.
Increasing upward mobility through a cross-disciplinary research lens.
Support the open-access and connection of data to enable novel insights and analysis.
Champion high-impact cross-disciplinary data and research projects that have the potential to deliver fast, low-cost results.
Support the development of data science talent in the social sector, helping organizations add a data-centric approach to their work.
Seed the development of low-cost, technology-driven innovations based on evidence-based insights to improve research, practice, and policy.
Does strengthening access and connectivity to education and broader datasets reveal new insights and translate to actionable strategies to help communities?
How do we increase demand for high-quality interoperable platforms that are adaptable to national and regional contexts?
Does building social sector data talent lead to strengthened data analytics and application that ultimately leads to better outcomes for children and families?
What resources and incentives are required to create a sustainable data talent pipeline in the social sector?
What is the most effective way to promote cross-sector data linking and analysis?
 Actionable Intelligence for Social Policy (AISP), National Neighborhood Indicators Partnership (NNIP), Data Quality Campaign (DQC), National League of Cities (NLC)