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Early in my career, I was working on a project using education data, and we were having a meeting with policymakers, school principals, and a team of researchers. When one of the principals asked a question about what assumptions were used in crafting one of the models, I was perplexed when the lead researcher launched into a long speech chock full of vague, unusual and complex jargon. Why would he do this? I knew that it wasn’t because the researcher was unable to discuss these ideas in simple, clear language because we had had many easy, plain conversations together about this very question. What was going on?

I would soon come to recognize this as an extremely common equity issue in data. This researcher felt attacked (or at least challenged) and so he unleashed his favorite power move: Jargon.

When I say “jargon” I mean technical and pseudo-technical words and terms used in data science. Jargon can be used to give power or hold power.

Sometimes using a precise and technical term is the best way to communicate an idea in data. These are important words that are worth explaining to our colleagues, clients and audiences to increase clarity and data literacy for everyone involved in our work.

Good data jargon gives power to our stakeholders because it reveals important concepts that are at the heart of our project designs, analyses and result interpretations.

More often I see jargon used to hold power. There are many data workers who feel more comfortable couching their decisions in a haze of unintelligible or overly complex jargon.

When we use unnecessary jargon, often our main goal isn’t to communicate the information, but rather to communicate how hard our work is, or why we are right, or how only we can understand the process or results we’re talking about. In the worst-case power-holding approach, it’s used to intentionally confuse audiences and hide mistakes or the equity impacts of our decisions. Jargon is also used to create intentional or unintentional barriers to understanding data, gatekeeping and holding the power within certain circles like a secret code.

So, sometimes we need specific words to best convey specific ideas, but in many other cases, we should communicate in terms that are more accessible, less esoteric, less misleading and more transparent – while being just as correct, exact and specific. I want to be clear that I’m not advocating “dumbing down” our data communication. It’s the exact opposite. It takes a great deal more skill and intelligence (not to mention vulnerability) to express your ideas in a clear and equitable way.

Now, because We All Count has pledged to never point out a problem without offering a concrete thing we can do about it; we have created something called The Data Jargon Decoder. It’s a crowdsourced lexicon of sorts, in which we divide commonly used data jargon into three categories: terms that We All Count think everyone should know; terms that have misleading or multiple meanings (especially confusing lay meanings); and terms that we think are unnecessarily confusing, redundant, inappropriate, or unnecessarily technical.

We’ll also provide a brief explanation of how the word gets used so that if you come across someone trying to hide behind jargon, you can get a handle on what they are trying to say (or not say!). Anyone can submit a term that they would like categorized and defined and we’ll keep updating the Decoder tool with more and more terms. Requesting a term is a great way to dip your toes into getting involved with the We All Count Project for Data Equity, as the tool will only be as useful as the number of terms it has. If you can remember a word, term or phrase that made you scratch your head, please help others by submitting it to the decoder!