I am the data – Part Two.

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.

Let us continue what we started last week – exploring what it means to be in the data.

You and I started this conversation by acknowledging how being in the data can be?

• scary and lonely to one group vs. empowering to the other,
• inevitable in this day and time,
• complex because of the ways it is captured and used,
• beneficial or not, depending on how it is applied in the algorithms designed to guide us.

The intention behind clarifying what being in data means or should mean is not to produce a "how-to" guide for us. Nor is it to dazzle us with "new, unheard data points".

The intention here is to be brave and creative in embracing and challenging the messy data around us – data that's you and me, about us. Data that's plain, old, fundamental, and frankly always existed.?

Example:?

• data collection that shows accountability toward anti-racist theatre systems (as laid out in the?We See You W.A.T)
• building visualizations of data points around criminal cases from immigrant communities
• building machine learning-based models to find patterns and the likelihood of?missing?critical data points

None of them are straightforward, yet all offer wisdom to bring clarity as to what being in data means.?

Remember those?6 community-centric data principles? Today, let's expand that list with 4 more principles. These principles are coming out of community conversations and reflection from the pure gold of a book I am reading currently (the same book that's the inspiration behind these current editions – more about which I will include with images in the next edition).

Continuing that list:

#7. Data we collect, especially about people and their communities, is meant to provide agency to those people – so they can meaningfully engage or resist, as necessary.

Example:?Data about trans artists should not be merely used to claim brownie points of DEI agent by an organization. That data should be accurate, wholistic, and humanly collected to allow trans artists to express their access needs without fear.

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#8. Data we collect can gather bias from multiple entry points, and we must be careful in managing it – both in the immediate and future next steps.

Example:?Let's visualize those multiple entry points of bias.?

Say, three datasets are collected about BIPOC immigrants – their housing, salary, and professional development opportunities. "Dataset" refers to the collection of data points. That data points can come from lists, tables, figures, and texts.

Here is a visual that comes to my mind:

Now, bias in this data can potentially come from one or more of four scenarios:

• Datasets A, B, and/or C are missing.
• Individually datasets A, B, and C are collected in a way that has become too complicated to merge them all.
• Elements of datasets A, B, and C (i.e., lists, tables, figures, and texts) are missing.
• Ways in which?missing?elements + datasets are collected, managed, or ignored.
• Ways in which the?existing?elements and datasets lead to decisions, strategy, and actions.

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#9. Data we choose to collect, analyze, and act on has consequences that you and I cannot deny. How data point is collected, transformed, and told – none of it can be seen in isolation. To see it in isolation allows complacency and can potentially cause harm.

Example:?Say you conduct a community survey to share the impact of your program with your funders. Let's see some numbers in this hypothetical situation. First, you opened the survey to 10K+ people. Out of it, you received 200 responses. Of those 200, 10 identified themselves in ways that don't directly "fit" in any of those funder's requested templates. What do you do? Change your data collection tool? Remove that question altogether? Or ignore those 10 responses? We will explore concrete ways to tackle this in the next edition, but for now, it is essential to understand that neglecting those 10 responses is NOT an option. Any action that ignores, removes, or reduces identities in favor of financial or any other such benefit has consequences of harm.

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#10. The harms of data can be both short-term and long-term. As a non-expert, evolving, continuous learner, you and I must commit to acknowledging and highlighting times when such harms occur.

Example:?Short-term harm could look like ignoring the access needs of specific identities or communities. Long-term harms could look like changing behaviors to cater to algorithms around us, without appropriate questioning as and when needed. Perhaps let's dig deeper into more such examples in the next edition!

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This is not a one-time, one-size-fits-all list.?

This conversation is an attempt to bring structure for all those moments to come when our decisions around data have far more implications than we understand today.

You and I need to learn to recognize harms in the "we" and create space for empowered and accountable "I".?

Because?We are?I am the data.

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

Today, I want you to consider what data means to you personally? If and when has it empowered, celebrated, and challenged you? What did you do?

?*** If you (or anyone you know) would be interested in being interviewed (i.e., very friendly convo with me - about work, data+ equity, and all life things in between) and highlighted here,?fill out this form.