Data Literacy Representation: Someone Like Me

We can all agree that representation matters. In the past year alone, representation has been at the forefront of our minds knowing many of us don’t see ourselves in media content or successful positions. It’s powerful seeing others who look like us or relate directly to our interests in these types of modalities.

Representation isn’t only about how a person looks. It can also be about seeing a visible hero share a similar path or having a relatable identity. Frequently in data literacy, there’s a false equivocation made that data literacy=higher education, when this couldn’t be further from the truth.

If we assume higher education is the price of admission to be data literate, then we’re already on the path toward failure.

Representation in data literacy means seeing another individual succeed in the data sphere without a degree. It means seeing someone impoverished finding their way forward and not always having it easy. It looks like someone who didn’t have the “popular” job titles and did what a person had to do to make ends meet. That is what representation looks like to me, but what it looks like to you can be completely different.

It means acknowledging many of us come from privilege and accepting many don’t.

And that is ok.

It means empathy.

It means different ages, backgrounds, education – it’s everyone. It means opening your mind to the possibility that skills can come from a myriad of places – but we can all end up in the same place, bringing different strengths to the table.

It means letting go of our egos and believing data literacy success only looks a certain way.

Data literacy looks like you, it looks like me, it looks like your neighbor. It looks like the cashier who rang up your groceries. It looks like the food service worker you bought your meal from. It looks like your aging parents and your young children. It looks like people you have never met.

Data literacy looks like all of us because it is all of us.

How do we start to make this change? Let’s elevate and normalize non-traditional paths and diverse stories and backgrounds. It means looking at how education systems work in different countries and how people may not have access to the same education. What is accessible in the US is very different than what’s accessible in Canada, and they share a continent. It means normalizing non-traditional education, so we continue to close the world’s data literacy gap.

  • Let’s normalize GED’s.
  • Let’s normalize gaps in employment history.
  • Let’s normalize not having a college degree.
  • Let’s normalize service industry jobs.
  • Let’s normalize aging in the job market.
  • Let’s normalize multiple positions on a resume.

Let’s acknowledge that representation in data literacy isn’t what it should be, and everyone deserves a seat at the table.


  1. Catherine Probst says:

    Well said. And ’nuff said.


    1. aldatavizguy says:

      Agreed Catherine! Thanks for taking the time to check out our blog!


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