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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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