Framework for Centering Racial Equity Throughout the Administrative Data Life Cycle

Abstract

IntroductionData integration by local and state governments is undertaken for the public good to support the interconnected needs of families and communities. Though data infrastructure is a powerful tool to support equity-oriented reforms, racial equity is rarely centered or prioritized as a core goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling. Generally, institutions have not adequately examined and acknowledged structural bias in their history, or the ways in which data reflect systemic racial inequities in the development and administration of policies and programs. Meanwhile, civic data users and the public are rarely consulted in the development and use of data systems.ObjectivesThis paper presents a framework and site-based examples of “Work in Action” that were collaboratively generated by a civic data stakeholder workgroup from across the U.S. in 2019–2020.MethodsPurposive sampling was used to curate a diverse 15-person workgroup that used participatory action research and public deliberation to co-create a framework of best practices.ResultsThis framework aims to support agencies seeking to acknowledge and compensate for the harms and bias baked into data and practice. It is organized across six stages of the administrative data life cycle—planning, data collection, data access, use of algorithms/statistical tools, analysis, and reporting and dissemination. For each stage, the framework includes positive and problematic practices for centering racial equity, with site-based examples of “Work in Action” from across the U.S. Using this framework, the workgroup then developed a Toolkit for Centering Racial Equity Throughout Data Integration, a resource that has been broadly disseminated across the U.S.ConclusionsFindings indicate that centering racial equity within data integration efforts is not a binary outcome, but rather a series of small steps towards more equitable practice. There are countless ways to center racial equity across the data life cycle, and this framework provides concrete strategies for organizations to begin to grow that work in practice.