The E-W framework provides guidance on seven leading data equity principles as a starting point for data users to approach education-to-workforce (E-W) data safely, securely, and in service of educational and economic opportunity for all. Data equity principles seek to ensure data are meaningful, accessible, and actionable for communities too often left out of data-driven decision-making processes.
Data equity principles offer necessary guidelines for data users to ensure data are meaningful, accessible, and actionable—thereby minimizing the risk of harm while maximizing the potential to promote more equitable outcomes.
What is Data Equity, and Why Does it Matter?
Data can empower practitioners, policymakers, and community members to make decisions grounded in evidence, but they can also reinforce deficit narratives, biases, and other long-standing structural inequities when used inappropriately. Whether intentionally or unintentionally, data can be misused and misinterpreted, sometimes causing harm to communities already most marginalized. People and groups who work with data must be aware of these risks and apply an equity lens to every phase of the data life cycle.
Historically, E-W data have been used in both harmful and helpful ways.
- Helpful: Disaggregated education data have shined a light on the needs of particular groups of students, informing the passage of landmark policies such as the Elementary and Secondary Education Act, which established the Title I program to provide funding to schools with a high percentage of students from low-income households.
- Harmful: Data on disparate academic outcomes, often referred to as “achievement gaps,” have been used to argue the inferiority of specific racial groups, primarily Black and Indigenous people, and reinforce deficit-oriented beliefs that blame individuals rather than the systems that generate advantages for some groups and not others.
Data are not inherently neutral. Like any tool, they require thoughtful use to achieve the intended goals. Modern algorithms built on E-W data are used in ways that can positively or negatively affect individuals depending on their use. Schools that have implemented Early Warning Intervention and Monitoring Systems to identify students at risk of not graduating for additional support have reduced chronic absence and course failure rates more so than schools without such data systems. But unintended consequences can occur. For instance, after in-person exams were canceled due to the COVID-19 pandemic, the International Baccalaureate program’s decision to use a data algorithm to predict students’ grades resulted in systematically lower scores for high-achieving students from low-income households who had expected to earn college credit and save money on tuition.
Using data in service of equitable outcomes means that at every stage of the data life cycle, users must think critically about both the possible risks and possible benefits data might bring to the communities that provide data yet too often are left out of the decision-making processes their data is ultimately used to inform.
How Were the Principles Developed?
This resource draws on considerations gleaned from multiple sources, including publications by data equity experts and input from partners involved in E-W data systems. We began by conducting a literature review to gather information on how data equity principles are currently defined and applied in practice. Next, we presented an initial synthesis of this literature to a diverse range of partners, including education and workforce policymakers and data strategists, researchers, equity advocates, and parents and educators who make—and feel the effects of—data-driven decisions. This two-pronged approach incorporates scholarly, practitioner, and lived-experience perspectives into the data equity principles.
Who Is This Resource For (and How Should It Be Used)?
Data equity principles are relevant to a wide variety of types of data users and data projects. Policymakers, administrators, educators, community leaders, and researchers who use data to diagnose disparities, implement evidence-based decisions, and evaluate the impact of policies, programs, and investments to address those disparities can apply these principles. Applying data equity principles in practice can be complex, and best practices can take many forms depending on the specific context or community. For each of the seven core principles, we offer reflection questions and potential pitfalls for data users to consider, examples of how to apply the principles throughout the data life cycle, and additional resources to consult.
The order in which the principles are listed does not reflect relative importance—all seven principles must be put into action to achieve data equity. In particular, Principle 7 is critical to successfully implementing all of the other principles and meeting equity goals.