Data Ownership, Governance, and Sovereignty for Nonprofits

What are the data points that describe you? Do you feel they accurately represent who you are? When you give away your data, do you know what it’s being used for? Do you trust institutions to make determinations about using your data? 

Data can be a tool for immense social benefit, but it also has a history of being exploited, manipulated, and stolen. This can make individuals and our communities feel less safe and more surveilled, and can uphold oppressive policies and systems. 

As a part of ongoing data justice work within the Wilder Foundation, I sought best practices for integrating data justice into our organizational structures based on insights from community organizers, data practitioners, and Indigenous Peoples. 

Here’s what I found that can help other nonprofit organizations using data in their program evaluations and community-based research. 

Strategy #1: Data privacy and security

Upholding robust privacy and security measures for personal data is essential to maintaining trust and protecting the rights of community members and program participants. Organizations should collect only the minimum amount of data needed for evaluation, minimize access to data, and dedicate comprehensive training to staff working with both electronic and physical data management systems.

Strategy #2: Data quality and accuracy 

Organizations should implement clear data collection processes and ensure each piece of data being collected has a clear, meaningful purpose. In the case of missing data, prioritize data quality rather than data perfection. Be transparent about what data you don’t have and avoid using assumptions to append or interpret data, especially when dealing with data on race and ethnicity. 

Strategy #3: Community engagement and participatory governance 

Intensive engagement is not always feasible or desired by the people you’re collecting data from. However, organizations should engage communities when developing data governance systems, when appropriate. Create space to gauge how people feel about their capacity for control and decision-making over their personal data. Incorporate the perspectives of data collectors and service providers working directly with communities into data ownership models.

Strategy #4: Accountability and transparency 

When you give your data away, it’s reasonable to hope that it will be used in a way that benefits you, rather than harms you. Organizations can navigate this responsibility by adopting trauma-informed data collection methods and committing to taking action based on findings that prioritizes community needs and improvements to programs and services. Organizations should also avoid data sharing or analysis methods that perpetuate systemic harm, such as through assigning risk scores and other indicators to populations without context. Lastly, make sure participants understand what data are required, which are optional to disclose, and how the data will be used.

Strategy #5: Share back of results 

An important element of data justice is intentionally sharing evaluation findings with all invested parties, including community members and participants who provided their data. While disseminating results and presenting findings to leadership and staff, organizations should frame analyses within existing and historical social contexts to avoid perpetuating harmful stereotypes about communities. Dissemination should also prioritize communicating disaggregated data and analysis to better ensure that results are as precise and actionable as possible. 

Indigenous data sovereignty 

When partnering with Indigenous communities, evaluation must be grounded in Indigenous self-determination. Data sovereignty is an Indigenous-led movement that calls for institutions to uphold a data governance model that adheres to certain data rights reflective of Indigenous evaluation and enduring legacies of colonization.  

The Indigenous Evaluation 101 Guidebook created by Wilder Research and Bowman Performance Consulting lays out key components for Indigenous evaluation, including:

• Learning about and incorporating communities’ culture and values
• Prioritizing community participation and engagement when appropriate
• Utilizing a strength-based framework that encapsulates Indigenous worldviews
• Designing evaluation to produce positive benefits for communities
• Reflective practices 

Researchers must consider the legal jurisdictions, processes of obtaining permission, and approaches to evaluation unique to each Indigenous community and Tribal nation. 

As a part of Wilder Research’s ongoing study of homelessness on American Indian reservations, researchers sought and received approval from tribal governing bodies and worked in partnership with the Minnesota Tribal Collaborative on study outreach, volunteer recruitment, and interviewing. Additionally, each participating tribe is the sole owner of its data, which they then use to advance efforts and enact change towards ending homelessness in their community. 

There is no universal guidebook to perfect your data governance model. However, incorporating intentionality and care into each step of the process can help pave a path forward to better ensure that we’re using data to empower and uplift our communities.

Read more about data justice and data sovereignty: 

• Digital defense playbook: Community power tools for reclaiming data – Our Data Bodies
• Building Consentful Tech – Allied Media Projects 
• Data Justice Brief – Metropolitan Alliance of Connected Communities