We Are The Data.

Welcome to data uncollected, a newsletter designed to enable nonprofits to listen, think, reflect, and talk about data we missed and are yet to collect. In this newsletter, we will talk about everything the raw data is capable of – from simple strategies of building equity into research+analytics processes to how we can make a better community through purpose-driven analysis.

When I hear the word "data", this is an image that comes to my mind:

This is an art display from the Vancouver Public Library's display (Summer 2023).

Doesn't it look like the data we have in databases? Here I see: a mosaic of multiple individual data points, neatly (or perhaps not so much), part of our CRMs — boxed in, indexed, formatted, and very much alive – if only we learn to read the story.

For some, data feels like an abstract entity—a stream of impersonal numbers, percentages, and statistics flowing through databases, missing the complete picture. For others, data is a living, breathing part of existence. It shapes decisions and impacts entire communities.

Here is the truth we tend to forget behind impeccable user experiences: we are the data.

Every data point, every dataset, reflects a piece of someone's story, a fragment of their experience. The moment we forget that, we fail to center humanity in our data practices.

You and I have been part of more databases than we care to realize, remember, or even know about. Different times/points in our lives can be seen in various X-digit numbers or X-character alphanumeric strings assigned to us (in registrations, IDs, case numbers, nationality status, and such). Try this: pull 5-7 data points from your school/college registration, employee ID, visa number, and other similar significant data, and narrate your story between those data points. I am sure you will find moments of being proud to be scared in that story.

So, I say this not lightly but with care: living as data can feel scary.

This is why, in this essay, I am laying ten truths here I like to call community-centered data principles. (we covered this two years ago, but it's time to bring it up again)

Through this, I invite you to a conversation centered on care, community, and context.

Because we are the data.

Not just "they," not just "others." You and I.

And when we forget that, we risk treating people like entries in a database rather than full humans with histories, futures, and agency.

Here they go:

1. The data we collect must center the people impacted by that data, especially those usually marginalized by design.

Example: if you plan to collect data from the stakeholders for strategic planning, include some representatives directly from the community you serve. We must learn to include our community both - when we need to collect numbers for impact and when planning the next strategic and sustainable steps.

2. The data we collect, analyze, and consume must empower and acknowledge generosity from all voices.

Example: To broaden your understanding of engagement and philanthropy trends toward your mission, design your actions by including all people and not being limited by questions/population samples driven by upper-limit giving thresholds. Generosity comes in more than one form, and we need to account for it. On some occasions, it might involve changing the engagement analysis formula; on others, it could include tweaking the data collection parameters.

3. The data we collect must prioritize the community's needs over the intentions of a few sponsors/stakeholders.

Example: Every occasion you get to evaluate what data we are collecting for all forms of analysis/AI tools, discuss and define the why behind those data points collected vs. those that are retired/uncollected. The more we become comfortable with this why-driven struggle, the better we will learn to center the community's needs.

4. We must understand that a single data point is just numbers and text. It alone cannot create change. The history, context, and narrative around that data point empower us to make change. So, 

a. The data we ultimately consume must transparently build accountability on/from all involved stakeholders,

b. The what, what-next, and how decided from the data are just as crucial in centering the people.

Example: how you hold the space for conversation from the output of data analysis (given by internal or AI tools), who is involved in the discussion, and what actions are taken next – all that matters. Collecting data by itself does not create an awareness of accountability.

5. When the data we collect represents our people, we must prioritize ethics from collection to building narratives as humanly as possible.

Example: Words we choose to communicate matter. Therefore, the words we use to collect data, say in the surveys and draw insights into the reports matter. Those words give or diminish power to all those impacted by the outcomes of such a report.

6. As data collectors, scientists, technologists, consumers, and in any other role, our data expertise is constantly evolving. We are evolving continuous learners of our community's needs and the ethical practices of data, thus affecting the collective knowledge we possess.

Example: We will never be perfect in our data design and solutions. We can, however, commit to iteratively improving and adapting to the community's growing needs. Let's add that humility in the way we interact with data.

7. The data we collect, especially about people and their communities, is meant to give those people agency so they can meaningfully engage or resist as necessary.

Example: Data about trans artists should not be merely used to report to funders. That data – first, should be holistically and humanly collected – to, second, allow trans artists to express their access needs without fear.

8. The data we collect can gather bias from multiple entry points, and we must be careful in managing it—both in the immediate and future.

Example: Three datasets are collected about BIPOC immigrants – their housing, salary, and professional development opportunities. When comparing insights to pull a story, we must understand the gaps in the data that could have led to biases (i.e., where were the assumptions made?, where were the missing points approximated, where were data points statistics-ified to be neat and complete-looking)

9. Data harm can be both short-term and long-term. And both deserve our attention.

Example: Some harms are visible immediately, like when survey options don't accommodate someone's identity. Others take years to unfold, like when algorithms start shaping behavior in subtle, unexamined ways. Our responsibility is to notice both and care enough to respond.

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I want you and me to be deeply aware of how we can own our relationship with data. We should truly understand the impact and implications of engaging with it as individuals.

Because all those times when, say,

• "95%" seems more significant than "5" (issue: context),
• the design of social identity questions is centered around the needs and understanding of funders, donors, and grant-makers (issue: de-centering community),
• the algorithms prioritize "efficiency" over the harms of racial bias (issue: data safety),
• people are categorized, segmented, and scored as an act of "smart strategy" (issue: reducing context),
• and many more such ways we can have and act around data

– we are making a choice.

A choice whether or not to exclude, disrespect, harm, or alienate someone through data. 

So, here is my ask: Own your relationship with data—not in a sterile, technical sense—but in the full sense of being alive and accountable.

We are not at war with AI — unless we choose to surrender our humanity in the name of optimization.

And we are not helpless against systems — unless we refuse to question how they're built.

Let's remember who we are—not just data users but the very people who shape it.

We are the data.

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*** So, what do I want from you today (my readers)?

Share with us: How do you put care in your work/relationship with data?