What Buddhism and Data Visualization Have in Common

"The less you judge things—including the contents of your mind—the more clearly you'll see them." — Robert Wright in Why Buddhism Is True

Buddhism teaches us to see the world as it actually is. Most of the time, we don't.

We perceive the world around us filtered through layers of thought, assumption, and learned concept. These filters serve a purpose: they are shortcuts that allow us to process information quickly and efficiently. But they come with a cost. We don't experience a tree; we experience our idea of a tree. We don't listen to a song; we hear our memory of every other time we've heard it. The practice of mindfulness is, at its core, an invitation to set those filters aside and perceive what is actually there, directly and freshly.

Data comes to us encoded in words and numbers, a format most of us, who don’t have a statistics degree, find bewildering. So our impulse might be to hand it over to AI and ask it to explain what's going on. But here's the catch: your questions are limited by your knowledge and experience. And AI’s answers are limited to it’s model training. You're seeing your data through filters that you may not be aware of.

Data visualization provides a less filtered view of the data that you can actually understand. Charts encode information in color, shape, position, and size. Whereas our brains slog through words and numbers, they process these visual cues almost instantaneously. So well-designed charts allow you to explore the data rather than just confirm what you or anyone else suspected. You arrive at ideas you can trust because they came from a more direct, personal encounter with the data itself.

Of course, no chart provides an entirely unfiltered view of data*. Data visualizers make decisions about what data to show and how to show it which influence how others see it. But thoughtfully-designed charts offer something closer to what Buddhism has always pointed toward: a way to see what is more clearly.

* Here, I’m talking about understanding and seeing data clearly. Of course, whether the data, in turn, offers a clear understanding of what’s happening in the world is a whole other (incredibly important) story. Here’s an overview of how to assess data quality.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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AI Charts Can Fool You. Here's How to Avoid That.

When four of the world's leading data visualization experts used AI to remake the same chart, they were thrilled with the results. Aesthetically gorgeous. Impressive. Done.

Then someone checked the numbers.

Some of the key performance indicators were wrong. And here's the thing: these are not beginners. These are people who have spent careers thinking critically about data. Yet a polished-looking output threw them off. As one of them put it, "if something looks pretty, we love it, and it weakens our defenses against verifying the numbers."

That's the core risk of AI-assisted data work. It's not that AI always gets things wrong. It's that when it gets things wrong, it does so confidently, and the output often looks too good to question. Imagine asking AI to summarize your program's outcome data, and it reports that 68% of participants improved. Even though that number may sound plausible, it could be calculated on the wrong group. If you didn't pause to ask where that number came from, you might repeat it to a funder.

So how do you protect yourself? Ask AI one question at a time, and check each answer against what you already know before moving on. And always ask: does this result match what I'd expect based on my experience with my community or programs? If something looks off, trust that instinct. It's a signal to slow down, not speed up.

The good news: you don't need to be a data expert to catch AI's mistakes. You need to know your data and apply the same critical eye you'd bring to any summary someone handed you. The tools have changed. That habit of mind hasn't.

Drawn from Chart Chat 71: Supercharge v. Sabotage, with Jeffrey Shaffer, Steve Wexler, Amanda Makulec, and Andy Cotgreave. Watch the full episode here.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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What If Your Next Data Presentation Got Everyone Out of Their Seats?

Most of us share data in ways that inform. A data walk is designed to do something more: build shared understanding and spark collective action. And unlike a presentation, it keeps everyone moving both literally and figuratively.

The concept, developed by the Urban Institute, is simple. You print out a small number of charts (four to six, no more), mount them around a room as stations, and invite a diverse group of stakeholders to rotate through them in small groups. The physical movement matters. There's no passive back-row seat at a data walk. Everyone is on their feet, circulating, leaning in, pointing at charts, and talking to people they might never sit next to at a traditional meeting. By the time participants finish the last station, they've traveled spatially and intellectually somewhere new.

The discussion questions do a lot of the work. Not "what does this data show?" but "what does this raise for you?" and "what factors might contribute to this?" That shift from reporting to sense-making is where the real value lies, and it's particularly well-suited to nonprofits, whose work almost always involves communities whose lived experience needs to be in the room alongside the data. A data walk creates a rare level playing field where a longtime resident and a program director are both analysts, both contributing, both learning.

And the charts themselves matter enormously. Because each station needs to communicate quickly and spark conversation rather than settle it, your visualizations need to be clear, focused, and simple enough that a mixed audience can engage with them without explanation.

Rockford, Illinois used this approach in 2018 to mobilize their community around third grade reading. Eighty people showed up. People who came in thinking the problem had an obvious fix left with a much more nuanced understanding of why it didn't. That's the data walk at its best: not a presentation of answers, but a structured journey toward better questions.

Learn more: Rockford's Data Walk on 3rd Grade Reading by Sylvia Cheuy, Tamarack Institute.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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How To Change Their Minds With Data Visualization

Your stakeholders aren't coming to your data as blank slates. They're arriving with assumptions, past experiences, and mental models already firmly in place. And no matter how good your visualization is, it will struggle to land if it doesn't connect to what your audience already believes.

The solution to this problem isn’t better charts. It's better bridges. Here's how to build one:

1. Start with what they already know

Open with a truth your audience already accepts such as a shared observation, a familiar trend, a problem they've personally witnessed. This isn't flattery. It's alignment. You're saying: "We're starting from the same place."

Here’s what that looks like in practice: A workforce development nonprofit is presenting job placement data to a skeptical board that believes their young adult participants are struggling primarily because they lack technical skills. So the presentation begins with: "We all know our participants come to us with significant skills gaps. That's been true since we opened our doors, and it's why our training programs exist."

2. Introduce the tension

Now show them where their current understanding falls short. Not to embarrass, but to create curiosity. "Here's what the data shows that we didn't expect." Tension is what makes people lean in.

In our example, staff might introduce tension this way: "But when we dug into why participants were leaving jobs within 90 days, technical skills almost never came up. Supervisors kept flagging something else entirely."

3. Reveal the new insight as the natural next step

Your key finding shouldn't feel like a surprise attack. It should feel like the inevitable conclusion of a journey you've taken together.

So, staff might then say: "It turns out that workplace communication and conflict resolution are often the problem. These 'soft skills' predicted job retention far better than any technical credential. Here's what that looks like in our data."

4. Anchor it in something real

Close with a specific person, community, or moment that makes the data human. Facts inform the mind. Stories open it.

In our example, that might look like this: "Marcus completed every technical module we offer. He lost his first two jobs within 60 days over a miscommunication with a supervisor. After six weeks in our new coaching program, he's been at his current job for eight months."

The most powerful nonprofit data presentations don't just show what's true. They bring their audience to the truth one step at a time.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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What's A "Good" Survey Response Rate?

Recently, a client I’m building a data dashboard for asked me: “Our survey response rate was 30 percent. Isn’t that really good?”

I could see where she was coming from. Given how hard it is to get people to respond to surveys these days, 30 percent may feel strong—and it may be higher than what your organization typically sees.

But imagine she asked this instead: “I just bought a used car for $15,000. Isn’t that really good?” You’d probably say, “Well, it depends on the quality of the car.” If it’s a junker that can’t make it to the end of the block, it’s not a good deal.

Survey data is no different. A “good” response rate depends on the quality of the data. Rather than asking “Is my response rate good?” a more useful question is: “How representative is my survey data?” Your survey results might reflect what your clients, volunteers, or participants think and experience overall. On the other hand, they might mainly reflect the views of a small group of highly engaged, eager survey-takers.

To assess representativeness, compare your survey respondents to the larger group they’re meant to represent. Ask questions like:

• Do respondents reflect the broader group’s demographics?

• Are different programs, locations, or levels of participation represented?

• Are newer participants responding at the same rate as long-time ones?

Even a high response rate can be misleading if certain subgroups are underrepresented.

If your respondents don’t look like the larger group, here’s what you can do:

1. Talk to staff and clients.

Gather insight on why certain groups may be less likely to respond. Barriers might include time constraints, language, survey length, digital access, or lack of trust in how data will be used.

2. Adjust how you collect data from underrepresented groups.

Based on what you learn, consider strategies to collect additional survey responses such as:

• Offering the survey in multiple languages

• Providing paper, text-based, or in-person survey options

• Shortening the survey or breaking it into sections

• Using trusted staff or community partners to encourage participation

• Offering small incentives or emphasizing how feedback will be used

3. Be transparent about limitations.

Whenever you present survey results in data dashboards, presentations, your website, or social media, clarify who responded and who didn’t. For example:

“These results reflect primarily long-term program participants; newer clients were underrepresented.”

4. Apply lessons to the next survey.

Use what you learned to improve both response rates and data quality next time.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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