Data Equity Principles

Data users must evaluate data practices to determine whether they have the potential to contribute to greater equity, as opposed to reinforcing the status quo or even causing harm to communities already most marginalized. They must question whether they are addressing the underlying structural factors that perpetuate inequities, respecting the dignity and autonomy of all individuals, and maximize benefits while avoiding harm. At the outset of any data project, decision makers should identify and communicate the funding source and funders’ priorities, the types of decisions the data project will inform, the data project’s stated public benefit and equity goals, whether the data project meets the needs and addresses the concerns of the intended beneficiaries, and whether the data project could lead to unintended consequences or have racial equity implications. Decisions relying on data algorithms should be closely reviewed to ensure they do not have discriminatory or other unjust impacts. Involving community members in data governance, institutional review, and advisory structures can help achieve these goals. View Details

Data users must seek the consent of individuals and recognize them as the owners of their data. Acknowledging that data represent the lived experiences of individuals, protecting data from improper use and exposure, and returning the data to community partners are all critical to promoting equity and earning public trust. Data users must follow data privacy laws and respect data sovereignty, for example, of Native American Tribes. Data users should consult the individuals providing data to determine who can securely obtain, view, or use data and for what purposes, weighing the risks and benefits of both restricting and opening access to data. Individuals should be allowed to access their personal data, correct data about themselves, and opt out from certain uses of their data. Decisions around data access can be made by a governance body that represents individuals who provide their data, including proximate leaders who authentically represent affected communities. View Details

Data users must acknowledge the diversity of experiences among priority communities to uncover disparities that can be hidden in aggregate data. Data analysis may require multiple levels of disaggregation to capture the intersectional nature of individuals’ lived experiences. Thus, data users must collect data on multiple relevant background characteristics, guided by a contextual and theoretical understanding of root causes to avoid perpetuating existing stereotypes and deficit narratives. The E-W Framework offers guidance on key disaggregates to consider. In addition to disaggregating outcome data, data users should break out data on E-W and adjacent system conditions (such as funding) to reveal other underlying disparities. View Details

To address disparities along the pre-K-to-workforce continuum, data users must understand the local social and historical context behind these disparities. Data users must examine data on structural conditions; learn about relevant past policies, programs, and institutions and how they may have promoted or perpetuated racial inequity; and understand what members of priority communities see as the barriers to achieving equitable outcomes. Direct engagement with people with lived experience is key to conducting reflective root cause analyses focused on identifying systems drivers of disparities—not symptoms—and solutions to dissolve them. View Details

Data users must critically examine their methods and assumptions for collecting and analyzing data to ensure they do not inadvertently reinforce historical biases, deficit narratives, and power imbalances. Quantitative methods are sometimes viewed as being inherently objective, but data users must be attentive to these risks and question their own motives and biases, where the data came from and what they might leave out, and who they see as the experts on the data. When seeking to answer questions, data users should consider triangulating quantitative methods with other approaches to inquiry, such as collecting qualitative data from interviews or focus groups to capture additional insights or designing community participatory action projects that privilege community voice and participation. Gathering multiple sources and types of information can help counter the bias in any one data source. View Details

Data users must approach visualization with thoughtful consideration to the lived experiences the data communicate and to every detail used to present that information—including labels, colors, ordering, graphics, and icons—to ensure it is accessible to multiple audiences and does not reinforce stereotypes and deficit narratives. Information on the source of the data, when and why they were collected, and who they represent should accompany visualizations. This and other contextual information (for instance, centering the structural causes behind disparate outcomes being shown, either though narrative text or additional data on system conditions) can be key to ensuring that readers do not misinterpret or misuse data visualizations. View Details

Community partners are a vital resource for data users. As illustrated in all of the principles, engaging community members with lived experience is key to centering equity throughout the data life cycle. Data users should follow best practices for effective community engagement, which include defining clear expectations and roles at the outset of a data project; recognizing and examining the power imbalances between decision makers and community members; building in enough time for community members to engage meaningfully in the project; allocating resources to equitably compensate community members; and avoiding the risk of exploiting, tokenizing, or retraumatizing them. As much as possible, data projects should build community capacity to use data to advocate for change, for example, by co-designing projects that reflect the community’s values, histories, culture, perspectives, and voice. View Details

Data governance involves developing and monitoring guidance for how data are collected, stored, analyzed, processed, made available for public use, retired, reviewed, and returned to data providers with action-oriented steps. Inclusive data governance structures (such as committees, boards, or councils) involve representation from groups providing data, have a clear purpose, and have transparent, equitable decision-making processes. Effective data governance also requires trust and shared accountability. View Details