Participatory practices improve your data integrity and help you make a deeper impact
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.
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.
No one better comprehends the complexities of how problems intersect and overlap than the people most directly impacted.
Community members can help grantmakers better understand the social, political, historical, and personal contexts of issues. They can also articulate the ways systemic oppression and injustice impact their daily lives. Without that perspective, funders might overlook the root causes of a problem and invest in programs that only address its symptoms.
Taking time to understand the complexities of the problems communities face is the only way funders will craft solutions that support lasting progress.
No matter what causes your organization seeks to address, other forces are at play making and preventing change. If you don’t account for those forces, you might misinterpret the data that ties your work to specific outcomes.
For instance, an organization battling food insecurity might measure their impact partly based on anonymous community surveys that ask about food access. But what if Supplemental Nutrition Assistance Program (SNAP) benefit requirements suddenly shift to become more stringent? Food insecurity might go up, but that’s not because the organization's programs are suddenly ineffective. On the flip side, if local schools shift from a needs-based free lunch program to one that’s available for all students, food insecurity might go down, but the organization can’t take all the credit.
Misinterpretations like these can have real consequences. Many grantmakers shape future programs and funding priorities around solutions that have proven to be effective. If funders aren’t getting a clear picture of how their work fits into broader efforts to make change, they might prioritize programs that aren’t effective and underfund programs that are.
Community members can play an integral role in helping funders understand the complex relationships between efforts to make change and their results.
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.
Your data might not show clear connections between inputs, outputs, and outcomes. It’s important to understand how the investments you make lead to lasting change, but this can be hard to track from quantitative data alone.
Community members are well poised to understand how a grantmaker’s funding contributes to long-term outcomes. They are closest to the issues and they have the historical and cultural insights that can help clarify causal relationships.
Tying inputs to outputs and outcomes is an important piece of measuring impact. Community perspective is invaluable in making these connections.
Sometimes you don’t know what you don’t know. This can be a big problem for funders. It’s easy to assume your data captures the full story when it doesn’t. And you might not have any way of knowing if you don’t have community input.
Community members can provide an essential gut check. They can speak to whether data reflects the realities they see in their lives as well as in the lives of people around them. Without their input you might be relying on metrics that severely misrepresent what’s actually happening.
Funders shouldn’t overlook the role community members can play in ensuring data is accurate and thorough.
Combining a trust-based approach with a data-driven one requires intention, strategy, and iteration. Here’s how to get started.
Data should not be something your organization extracts from the community. Instead, your team should work to build relationships rooted in collaboration and dialogue. This requires you to involve community members from the very beginning.
To involve community members early on you need to understand how they communicate. For instance, if you’re trying to connect with rural community members, relying exclusively on email might not be the best tactic. In rural areas, about 1 in 4 Americans don’t have access to reliable home internet. It might be worth attending (or hosting) a community gathering or event to connect with people directly. Community members are much more likely to engage if your team makes the effort to meet them where they are.
Before you start collecting data, your team should work to open up lines of communication with community leaders. Ask them about what their priorities are and how they see your organization fitting in. Be transparent about your intentions and invite community members to share their feedback and concerns.
Kim Bui, a journalist with the Arizona Republic, puts it this way: “It is important to tell stories with a community rather than on behalf of them and to seek out what they would want to learn and what would be useful for them along with their concerns.”
It is important to tell stories with a community rather than on behalf of them and to seek out what they would want to learn and what would be useful for them along with their concerns.
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.
The communities you serve should be able to see themselves in the data. First and foremost, use language that reflects how the community self-identifies. If you’re using names or labels that don’t align with how a community describes itself, their relationship with the research might feel contentious from the start. If you’re not sure what these names and labels should be, ask community members.
You should also make space for identities that intersect and overlap. It may seem like a small tweak in terms of data collection, but letting people identify as more than one race or ethnicity can be important in honoring their full identities. No one wants to erase some portion of who they are to fit into a box on a survey.
Though it can be tempting as a catchall, be careful of lumping people into an “other” group. That label can be very alienating and can show that you’re not all that interested in investigating a group’s experiences.
Keep in mind that the metrics you collect and analyze often represent people. Use language that centers their humanity. The phrases you use to describe the data can also do a lot to frame (or neglect) the systemic forces at play. For example, look at these two iterations of the same graph exploring the racial disparities of mental health diagnoses for incarcerated people. In the first, “people” are reduced to “inmates”, and the generic title obscures the findings of the data. In contrast, the title of the second graph names the systemic injustice at play and provides a clearer takeaway.
How you label your data shapes how your audience perceives it. Source:D’Ignazio and Klein (2020) “Disparities in Mental Health Referral and Diagnosis in the New York City Jail Mental Health Service.”
As you collect and organize your data, you need to consider how you represent small populations. If you use big units of measurement, you might reduce some populations to 0%. For example, Native Americans are often invisible in many state and federal data campaigns. The NCAI Policy Research Center explains it this way:
“American Indians and Alaska Natives may be described as the ‘Asterisk Nation’ because an asterisk, instead of data point, is often used in data displays when reporting racial and ethnic data due to various data collection and reporting issues, such as small sample size, large margins of errors, or other issues related to the validity and statistical significance of data on American Indians and Alaska Natives.”
Being intentional about how you collect, organize, and label your data to reflect the communities you serve and make space for their full identities will go a long way in helping community members stay engaged.
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.
Data should never be used to discount or override community voices. As you look to move from insights to action, you want a feedback loop that takes into account community voices and keeps lines of communication open.
Just saying that you're open to feedback isn’t enough.You need to build a mechanism for community feedback that fits how community members tend to communicate. A few options to consider:
A dedicated email address
Direct outreach to talk to people in person
Partnering with local institutions, such as universities, hospitals, or nonprofits, to help you maintain these relationships
You’re not only aiming to collect feedback. You also need to be responsive to feedback you receive. Community input should influence not only data collection and analysis, but also how you create new programs or initiatives in response to those insights.
Don’t think of community input as a one-time thing. Rather, view it as an ongoing process.
Grantmaking organizations can’t ignore the power that data holds. Data science and analytics can transform how grantmakers understand their impact and reduce the burden on grantees. But without a balanced approach that centers community voices, your data strategy will likely come up short.
For some funders, part of the work will be stepping out of the binary way of thinking. Participatory practices and data-driven decisions are not mutually exclusive. It’s time to embrace a new combined approach.
As you look to incorporate community voices into your data strategy, be sure you choose the right tools to support your work. Pick a grant management software that allows you to collect meaningful data and build deep relationships with your grantees.