Trust-Based and Data-Driven Grantmaking

As grantmakers shape their strategies and priorities, many are looking for ways to leverage data and build collaborative relationships with grantees. But too often, this is framed as an “either or” choice: either they can follow the data or center the community. 

These are not mutually exclusive frameworks. Grantmakers don’t have to choose between a trust-based approach and a data-driven one.

In fact, the most effective grantmaking strategy weaves together the two approaches to ensure that community voices provide context for data, and the data captures the lived experiences of community members. 

Engaging with and understanding a community's perspective requires buy-in from the community itself. Jonathan Schwabish and Alice Feng, authors of Do No Harm: Applying Equity Awareness in Data Visualization, explain how community involvement bolsters research: “This kind of buy-in—from members of the community, policymakers, and other stakeholders—can help the research be more impactful, relevant, and embraced by a wider audience.” 

In short, a balanced approach powers a virtuous cycle. Strong relationships inspire more buy-in, which improves the research. And better research strengthens community relationships. 

To strike a balance, grantmakers should adopt participatory or “culturally responsive” data practices. Researchers define these practices as “models which treat communities as research partners and involve them in the process from beginning to end, from defining research questions to collecting and analyzing data.” 

These ideas are exciting, but what does it look like to incorporate community perspective into your data strategy? Let’s dig in.

No data exists in a vacuum—this is a mantra in the world of data analytics. When it comes to drawing insights from the data, context is essential. Ignoring context can lead researchers to draw false connections, assume causality, and advocate for ineffective solutions.

Case in point: data without context has helped to perpetuate the criminalization of Black Americans. Black Americans are incarcerated in state prisons at nearly five times the rate of whites. For centuries, statistics like these have been used to uphold the false narrative that Black people are more likely to commit crimes than white people. But the context tells a different story. 

In reality, the discrepancy in incarceration rates stems from racist policies like Jim Crow, the War on Drugs, and “stop and frisk”policing. Without understanding this context, researchers might focus their work on personal behavior rather than systemic change and in turn help to support more racist policies. 

In the world of social impact, community input is an important lever to provide context for data. No matter how much data you collect, without community input, it might not mean much. You want to understand how the metrics you track fit into a broader story. For instance, it’s community voices that have helped illuminate the true lived experiences of Black Americans and the systemic racism that shapes their interactions with the criminal justice system. 

Without community voices your team might struggle to ensure that the data you collect:

• Reflects the lived experiences of community members

• Fully captures the success or failure of a program or initiative

• Accurately connects conditions to broader systemic forces

Plus, if the community feels included, there’s a much higher chance that they will embrace new programs informed by your data insights.

Structuring your data strategy to include community input is essential to advancing equity. It allows you to center the lived experiences of the people you serve, which enables you to make real, lasting change. Here are five ways incorporating community perspectives can help you make a bigger impact.

Quantitative data is essential for tracking progress. But often there are aspects of impact that don’t fit neatly into metrics. Seeking out community perspectives can give your team a fuller picture of how your programs are making an impact. 

One example of an insight that’s difficult to quantify is the community attitude towards your efforts. Do they trust your organization? Do they support your mission? If they don’t, it might mean you need to be more intentional about creating space for dialogue and investing in outreach. 

Community input can also help illuminate the unexpected impacts programs may have. You might be surprised by the ripple effect of your work. Making space to explore these unexpected outcomes will help funders get a more complete picture of how their work makes a difference.

Prioritizing qualitative data alongside the quantitative will help grantmakers capture the full effect of their programs.

Combining a trust-based approach with a data-driven one requires intention, strategy, and iteration. Here’s how to get started.

Your research should work to address community needs. If it doesn’t, you run the risk of creating an extractive relationship with the communities you seek to serve. 

Make time to learn about what the community values and what priorities they view as the most urgent. These findings should shape your program’s goals. Your team should be able to clearly articulate how the community stands to benefit from your research. 

Be sure to balance long- and short-term goals. You may have long-term research goals, but you also want to be able to offer something of value for the community members now. It will be hard to get people on board if the research won’t make an impact for decades. 

Part of understanding community needs is being mindful of historical context. If community members have had negative experiences with institutions in the past, that might influence their willingness to engage with your work. 

That doesn’t mean you shouldn’t include them. It means that your team needs to learn more. What issues did community members have in the past? What was missing from relationships with funders and researchers? Answering these questions will keep you from repeating others’ mistakes and will help your team better understand what their actions represent to the community.

Data justice is a principle that encourages researchers to consider data as a resource the community owns rather than something that is extracted. Instead of dictating how data will be collected and used, funders work collaboratively with communities to align goals, ensure privacy, and root out flawed data.

Part of embracing data justice is avoiding the scarcity mindset. You can’t use data up. Too often organizations extract data and hoard it away from communities as if sharing the data diminishes its power. But this approach is misguided. In fact, viewing data as a joint resource can help boost community engagement and strengthen the integrity of the data. 

This collaborative approach speaks to a key tenet of data justice: ownership. Community members should retain ownership of the data. Nothing should be shared publicly without their consent. Adopting data justice practices means researchers work with community members to decide which aspects of the data will remain private and what can be published. 

When data is shared widely, it should be done thoughtfully, without putting community members’ privacy at risk. Researchers should also avoid publishing data about a community just to bolster researchers’ careers and publications. Sharing the data should offer a clear benefit for the community.