Relational Data Practice

How to combine stories and data to demonstrate the impact of relational work.

Data matters. Whether you work in a frontline service organization, a tech startup with a mission, or a political campaign, data is probably at the core of how you, your bosses and your funders make informed decisions. To do your work, you need people to trust that that work is effective and data is one of the most important tools for earning that trust. It also lets you test your own assumptions about what is and is not working so that you can better create the changes you want to see in the world. Without data, trust is earned and decisions are made through a mix of anecdotes and charisma, both of which matter but can be dangerous, like data can be dangerous, if used exclusively. But data about relationship formation does not work like other forms of data. When our goal is to get a network of people to trust one another, support one another, and make powerful things happen together traditional data practices can be counterproductive. To understand why, and to understand the alternative, it helps to have an understanding of how relationships can and can’t be measured.

Why KPIs are the wrong way to measure community

Imagine a cupcake factory. You know exactly what you want it to produce: cupcakes of precise dimensions with just the right twirl of frosting on top that are delicious and pass food safety tests with flying colors. All of your measurements flow from this ability to predict your desired end state. How many cupcakes did you make? How many had flaws of some sort? How much did they cost on average? Measuring these things and tracking them over time helps you understand when you need to go in to tweak your factory or bring in better machines to make your cupcake production more efficient. Measurement is about understanding where to exert control to drive efficiency.

On a fundamental level communities are not factories, and if we try to measure and manage them like factories they break. To understand why, think about a relationship that has profoundly shaped your life. Was it predictable? Did it efficiently do exactly the thing you wanted it to do or did it surprise you and change what you wanted? Could you have, at the beginning of the relationship, established a concrete measurable goal and then used that goal years later to understand the relationship’s success?

This unpredictability is a fundamental property of all relationships. The more effective we are at building relationships, the less ability we have to predict exactly what they’ll look like. An ideal factory is predictable and tightly controlled, but engaging in relational work requires us to abandon predictability and relinquish control. This does not mean that successful communities descend into chaos. There are ways to reliably create strategically aligned outcomes that are unknowable at the outset. To chart a path to them, we need to think about data less like managers of a factory and more like stewards of an ecology.

There’s nothing wrong with establishing a goal related to a community and tracking it. It can be helpful for a political campaign to know how many phone calls were made or for an alumni network to know how many people donated. But if we make that goal the only thing that matters and put it on a graph, then we begin treating the community like a factory. When phone bankers come to us with a plan to mobilize support in their churches we’ll brush them off and tell them to get back to work. When the relationships in the community surprise us, which is what they must do if they are to thrive, we’ll see it as inefficiency and wrench them back into place. Relational data practice is about moving these surprises to the center of our work.

What to measure when we measure relationship

Relationships have a life of their own. If two people connect through a community most of the life of that connection will happen outside of that community. They’ll meet up for coffee, become friends and start inviting one another to cookouts. This blossoming around a community is a powerful sign of health, but most organizers only learn about it anecdotally.

Relational data practice lets an organization get systemic about understanding what is happening around their gatherings because of their gatherings. This creates a detailed picture of where the community is healthy, where it has the potential to grow, and where it holds emerging strategic opportunity.

To collect these data I like to ask questions that highlight different phases of a relationship’s lifecycle:


How often are people who met through the community spending time together outside of it? Who is meeting up and what are they spending that time doing and talking about?


How often are people who met through the community meaningfully supporting one another? Who is offering that support and what does it look like?


How often are people who met through the community envisioning and taking actions that support the community’s values? Who is involved and what actions are they taking?


How often are people who met through the community working through conflict together? What is the story of these conflicts, are they generative or are they pulling the community apart?

When people are new to a community gathering is a sign that they are becoming more deeply engaged. Once gathering, care is a sign that the relationships are maturing and becoming meaningful. Once care is happening, action is a sign that people feel a sense of shared values and purpose and feel supported enough to act on them.

Conflict tells a subtler but critically important story. Communities with no conflict are often brittle, we want to see signs of generative disagreement to know that the community is resilient. These conflicts also shape what the community will prioritize. At their core many conflicts are about values, and understanding how those values are expressed and prioritized lets us know what the community will and won’t do. For example, a community who has had a series of debates in which open source principles have won out is likely to produce open source software and unlikely to produce patents.

Observing where these relational lifesigns exist and where they don’t creates a nuanced picture of where a community is in its growth, how organizers can show up to nurture it, and why funders should invest in its potential.

How to collect relational data

Every community looks different, and organizations which build community have widely divergent systems for managing data. There is no universal best practice for collecting relational data, but there are a set of principles that are helpful for shaping a data practice that works.


Meet people where people are with short, open-ended surveys.

When someone is just getting to know a community learning a new digital platform can be a major barrier. If you are organizing professionals who are used to Slack, have them communicate on Slack and collect data from there. If people are comfortable on WhatsApp, use WhatsApp. If they primarily meet in person and aren’t comfortable with a shared digital platform use SMS or email.

On this platform, find a way to target people with one-question surveys that are quick to respond to. Because you know who you are sending each survey to you can correlate the answers over time, creating a clearer picture of your community without relying on long surveys that generate fatigue.


Gathering Surveys

After an event, create a simple, one-question survey that is automatically sent out over whatever channel your community finds most comfortable: email, SMS, WhatsApp, Slack, etc. Ask “have you had or do you plan to have a followup conversation with anyone you met at our event?” If they say yes, send optional followup questions to ask who the conversation is with and what it will be about. Associate the answers with their contact records in your CRM and create a feed of information about followup conversations tied to the event.


Pay attention to what’s happening in the margins.

Often the relational magic happens after an event is formally ended. Volunteers chatting at the snack table after a training, delegates hanging at the bar after a conference, members of a congregation engaging in fellowship after services. Relational data practice often focuses on these spaces more than on the core programming that an organization puts together, because in these spaces people are free to make their own decisions and those decisions reveal how relationships are forming.


Afterspace Analysis

After an event, invite people to a place where they can connect. Ideally have food, though with the right strategy these afterspaces can exist virtually as well. Pay attention to how many people stay and how long they stay. Find ways to debrief with staff and community leaders about the interesting conversations they had and capture summaries of those conversations wherever your organization stores data.


Get conversation reports from staff and community leaders.

Often the best information about your community sits in the heads of its leaders: your organization’s staff who focus on community, and emerging leaders who are hubs of relationship. Collecting this information in a systematic way can be burdensome however, it can happen through one-on-ones or by requiring people to fill out regular reports, but this can feel burdensome. Instead it can be powerful to let leaders signal an organization’s leadership in their conversations with one another.


Conversation Report Bot

Create a channel wherever these leaders like to communicate with one another (Slack, SMS, etc) and invite them to use it to share stories about interesting conversations that they’re having in the community with one another. Then add a bot to that channel which listens for any message with a hashtag. If a staff member or leader thinks that a conversation is significant they can use a hashtag when they share it. If they do, the bot will read the conversation, associate it with the records of anyone mentioned (e.g. “I had a great conversation with Alicia Martinez…”) and log it in your organization’s CRM. Any statements without hashtags will be ignored by the bot, so that leaders have control over what is observed.


Weave data collection into the practice of organizing.

Data about relationship flows through relationship. Often the information your organization has access to is shaped by the trust you have built with the community around you. For this reason data collection is most effective when it is tied to an offering that builds the strength of the community in some way. Avoid clinical surveys that break a sense of community or opaque surveillance that can erode a sense of trust.


Care Storybanking

Once a year, use the platform VideoAsk to record a message from a community leader expressing gratitude for everything that the community has done. At the end of the video, ask people to share stories of times that they have felt cared for by other people in the community. Accept answers in video format (or audio or text, depending on what your community finds accessible) and weave them together to reflect back to the community at a time of celebration. Share this video with funders. Also pay attention to where these stories come from and to who is mentioned in them to craft a picture of how care in the community is happening, follow up to build relationships with people offering care.

How to store, analyze, and interpret relational data

Organizations differ in where they keep data. Some use sophisticated tools like Salesforce, others get by on Google sheets and the knowledge in people’s heads. Data needs to move from the kind of methods mentioned above to some place where it can offer insight into what is happening across the community. This could be a CRM, though for small organizations lightweight tools like Airtable or Notion can work as well.

Moving Data

Ideally a bare minimum of relational data is entered into a CRM directly. Instead, data moves through API integrations. In some cases these can be managed with no-code tools like Zapier, in other cases Relationality Lab will write custom integration code (e.g. for more complex bots). We are in the process of refining the most useful of these integrations to publish as open source tools.

Heatmaps and Stories

As you collect data about who is gathering outside of your events, who is caring for one another and who is having conversations with staff your contact database will begin to become a heatmap of activity. Instead of an archive of mostly-equivalent names and email addresses you will be able to easily sort and filter to see who in your community is most relationally active, then click in further to see a feed telling the story of that relationship. Every week, senior staff will be able to glance at a succinct dashboard of the most relevant emerging trends and relational opportunities from across their community, while organizers close to the ground will be able to make informed decisions about which areas of relational opportunity to invest in.

Reporting to Funders

Relational data can help to craft a story about proficiency, possibility, and progress while giving communities room to maintain control of their destiny and operations. By showing, for example, that a community has a powerful track record of creating gathering and care around a particular set of values but has yet to meaningfully translate that potential into action organizers can let funders know why they are trustworthy and how investment can unlock enormous potential. In this way relational data can be a powerful tool for building trust between funders and grantees. It gives funders a way to understand that impact is happening and justify it to their boards without needing that impact to be predictable or precisely controlled. This allows funders to center the needs and strategic objectives of the communities they fund while maintaining a clear understanding of how their funding is generating impact.

Getting Started

Any organization which builds community already has a strategy for dealing with relational data, even if that strategy has emerged haphazardly. At a minimum a few people maintain context in their heads, a few spreadsheets of names and phone numbers float around in ways that may or may not be useful. Getting better is about improving on these systems to generate meaningful insights about how a community is growing and how organizers can show up to make it better. By looking at where relationships are happening and where stories about those relationships can be captured, communities can begin to paint a picture that can be transformative.

Want to learn more? We're happy to advise or work with organizations working to build communities that drive change.