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Data Visualization Guide

Choosing your Question and Audience


  1. Who is your audience for your visualization?
  2. What level of familiarity do they have with your topic?
  3. What is the purpose of your visualization?
  4. Is it to communicate a finding, or is it exploratory for your own analysis?
  5. What is the story that I'm trying to tell?


The first consideration that you want to make in creating your visualizations is the audience and purpose. For example the first two questions are important because then you can decide whether you want a detailed visualization for your peers or a simplified visualization for the public? The next two questions are important because then you can decide if there’s a main idea that you’re trying to communicate, is there an important trend, or a comparison that you need to highlight?

This is important because different forms tell different types of stories. This is also the place where you are considering context – where is your visualization going to be used? Is it for a presentation? Is it stand alone or part of an article? Sometimes if you’re creation visualizations for a publication, they often have guidelines for what kind of visualizations they want. So you might want to consider that as well. These are all considerations you want to make before even getting to the tools.

Inherent Bias

Often, when we are creating charts and graphs, one bias is that we think of ourselves as the ideal user. This is not only a problem because we know more about the data than the target user, but because other users might have a different set of constraints than we do. Moreover, there are never going to be an automatic set of things you can do to make the presentation perfectly accessible, so you need to carefully think through your target audience, invoke participatory methods and incorporate feedback and critique. This is why this workflow is iterative because as you share your visualizations with peers you will find spots where you can improve the understanding of it.

Tips for Visualization Uncertainties

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