1. Build Community Power

Community knowledge is paramount to accurately understanding problems and crafting solutions that work. Intentional community partnerships and thoughtful design processes strengthen community capacity and can result in stronger advocacy and better data tools.

2. Address Root Causes

Accountability tools should acknowledge the root causes of educational inequity, which are not primarily the result of individual actors but rather stem from structural inequities and systematic differences in the social, economic, & environmental resources & assets that affect educational outcomes and well-being.

3. Make Data Actionable

Data tools should inspire and support community actions toward policy and systems change. Offer viable solutions to improve material conditions that incorporate the experiences of marginalized people, including those for whom data may not be readily available.

4. Move Beyond Maps and Numbers

Maps and statistics are important staples of equity data tools, but different types of visualizations and qualitative data can help convey information that builds political will, supports community action, and provides a more comprehensive view of an equity issue.

5. Data Democratization

Open access to public data is critical for fair representation and transparency, and to allow advocates access to data they need and in a form they can use. Ensure that data is affordable and available to impacted communities to ensure long-term accountability.

6. Disaggregate Data

Understanding how conditions and opportunities vary for different groups, including their histories and the policies affecting them, is critical to developing and advancing tailored equity solutions. Disaggregate data by race/ethnicity, gender, nativity, ancestry, income, and other factors to the best extent possible.

7. Emphasize Assets and Opportunities

While uncovering disparities can be valuable for compelling action, it can have the unintentional effect of perpetuating inaccurate and negative stereotypes about communities. Data tools should also focus on community assets and strengths that can be built upon.

8. Context, Context, Context

We strive to make our tools as user friendly as possible by providing clear explanations of what the data shows, why it matters, and what users can do about it. We provide definitions of indicators and data sources.