Data Equity Principles Breadcrumb Home Data Equity Principles About Data Equity Principles The E-W Framework recommends eight core principles for centering equity throughout the data life cycle to encourage more ethical and effective data use. Profiles of each principle describe their importance for promoting greater equity and offer examples of how to apply the principles in practice, reflection questions and potential pitfalls to consider, and additional resources to consult. Get Started with the E-W Framework Data Equity Principles In-Depth Share Explore the E-W Framework Components Data Equity Principles Essential Questions Indicators Disaggregates Evidence-based Practices 8 Data Equity Principles 1 Employ ethical behavior to respect the rights of individuals who provide data, promote greater equity and well-being, and minimize the risk of harm. Data users must assess whether practices advance equity, address structural inequities, prevent harm, and involve communities in accountable decision-making. View Details 2 Protect the privacy of individuals who provide data while ensuring appropriate ownership and access to information. Data users must get consent, protect privacy, respect data sovereignty, and give people control over how their data is accessed and used. View Details 3 Disaggregate data on both outcomes and system conditions to analyze disparities, monitor progress, and guide action. Data users must disaggregate data to reflect diverse, intersecting experiences and reveal disparities in outcomes and system conditions. View Details 4 Examine social and historical contexts to identify root causes of disparities, inform data collection and use, and develop data-driven solutions. To address inequities from pre-K to workforce, data users must understand local context, structural barriers, and community-identified root causes. View Details 5 Question default methods and assumptions for data collection and analysis and triangulate quantitative data with other sources. Data users must question methods, address bias, and combine data sources and community input for fuller, more equitable insights. View Details 6 Ensure data visualizations promote inclusion and awareness across culturally, linguistically, and racially diverse audiences. Data visualizations should be accessible, contextualized, and designed to avoid reinforcing stereotypes or misrepresenting lived experiences. View Details 7 Restore communities as data experts using culturally responsive approaches to engagement and co-creation that support equitable data use. Community partners are essential to equitable data work and should be engaged meaningfully, compensated fairly, and empowered to drive change. View Details 8 Establish inclusive data governance structures and practices with transparent decision-making processes. Data governance should set rules for data use and access, with inclusive, transparent structures that build trust and shared accountability. View Details