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What is #dataqueer?

          dataqueer

          1. A queer-identified person who is geeky about data and shares an affinity with other queer data geeks.

          2. A philosophy or orientation towards data that focuses on (a) challenging the norms of a data-driven culture or the data industry and (b) approaching data collection, use, and maintenance a way that may seem counter to generally accepted principles

At a recent conference, I only half-jokingly encouraged people to Tweet about my talk with the tag #dataqueer because I kind of wanted to make it a thing. I’ve been thinking a lot about dataqueerness lately, and what it might mean in relationship to my work.

As someone who works in data with other queer people, and enjoys geeking out about data with fellow queers, sometimes just being queer and being into data is a social affinity that I can happily share with others. But I’m also thinking of dataqueer as an orientation or approach to data. Smushed with a prefix this way, queer is often used to imply critically challenging norms, destabilizing or decentering hierarchies and binaries, and applying creative redefinition to a particular area of focus. If we think of it this way, dataqueer could be an identity that signals a particular approach to data.

What Does It Mean to Queer Data?

One possibility is that dataqueerness is simply about questioning the central principles of a data-driven society or industry. In business, this could mean looking at how we can use data for something other than increasing profitability and revenue or reducing risk. In academia or policy or the technology industry, we might think about how we can both be data-oriented and also question the value of a data-driven society. Rather than thinking about privacy as an afterthought or an extra layer, following the core assumption that More Data Is Good, dataqueerness might mean always asking why we need data as a first principle rather than just how to collect more.

Being dataqueer might also be about focusing on data that is messier or less obvious to analyze, spending time on the unusual data points or outliers. It might mean thinking critically about established categories and instead looking for new and different ways to slice information. Why, for example, is it always 18-35? Why are gender categories in marketing data always male and female? (What is the value of gender as a marketing demographic in the first place?) Someone who is dataqueer might take risks in going beyond how the standard data professional would approach data, and instead think creatively, applying different values or looking for different outcomes. A dataqueer person might even use data to show the harms of collecting data, or work within data with the aim of destroying or complicating data.

These are just some nascent thoughts, of course. What does #dataqueer mean to you?

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Practical Tips for Trans-Inclusive Data

graph of genders in the colors of the trans flag with options "male," "female," and "annoyed with your question"

As this post goes live, I’ll be sharing a talk at AlterConf DC called “5 Simple Steps for Trans-Inclusive Data.” This talk originally crept into my brain as an idea for a very long blog post, and as I was preparing to cut that idea down to twenty minutes with Q&A time, I decided to also execute the original plan, since I can’t possibly say everything I want to about how to make data more trans-inclusive in fifteen minutes.

The post that follows is a detailed guide of specific steps you can take to make whatever data you work with more trans-inclusive, building off of the talk content. Skim through the list below and use any tips that you find applicable! I’m drawing from my experience working with member and donor data at national non-profit organizations, but you can apply this advice to any kind of human-centered data you collect including data on customers, employees, patients, survey respondents, and app users. My starting point here is that trans people can show up in any data set, and so it’s important to address the needs we have around privacy, comfort, and affirmation not as a special population but as a regular part of data strategy. Rather than othering trans people, consider our experiences an opportunity to improve your data collection, storage, and analysis practices for everyone!

If you’d like to hear more after reading the tips below, check out my speaking page for more information. I’m hoping to do more “dataqueer” talks and workshops in the future.

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