Evergreen Data ReBlog: Is Feminist Data Visualization Actually a Thing? (Yes, and How!)

How can data visualization be feminist? Data is data — it speaks for itself.

A charming idea, to be sure. But it just ain’t true. Feminist data visualization is (and must be) a thing because data, data analysis, and data visualization are never neutral. The premise that, if handled correctly, data can present neutral evidence, is deeply flawed. Culture is embedded into our data at every stage.

As long as humans have been thinking about data viz, we’ve been projecting our worldview onto it.

Guest poster Heather Krause with Datassist discusses the concepts underlying feminist data visualization, how different cultures interpret data, and what data scientists and researchers can do to account for these differences in world view when collecting, analyzing, and presenting information.

Read more here.

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Evergreen Data ReBlog: Color Psychology

As I usually do in my workshops, I talked to a group in Warsaw, Poland about how we should use color intentionally in our data visualization and that, in fact, the color choice itself can help us tell our story. I prepared a little activity around this issue, in which I asked them to generate a list of the words, phrases, feelings, memories, etc that come to mind when seeing this color:


Apart from “very yellow,” what words and phrases come to mind when you see this color? Do you have a positive or negative association to it?

You may be surprised to learn that auditors themselves don’t see the world in stark black and white, and we enjoy a dash of color just as much as the next person. Within reason, of course. (And for the record, our words are ‘sunflower,’ ‘sparky,’ and ‘neon sign.’) But what influence does color have on our perception, and how does that apply to our work?

Stephanie Evergreen explores the influence of colors on our psychological state, and ponders how they can best be used in data visualization. Read more here, or explore the psychology of color in more in-depth here.

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Stephanie Evergreen ReBlog: Qualitative Data Visualization (Gauge Diagram)

When we debuted our Qualitative Chart Chooser last November, we promised to dive into detail on specific visualizations, so let’s kick it off by discussing how and when to use one of the most derided charts of all: the gauge diagram.

I don’t know about your office, but we at OAD have a word for qualitative data: squishy. It’s squishy, and it’s smooshy. It’s sometimes indirect, and can be a real challenge to measure. Nevertheless, some of the most vital and telling information and evidence you can get might be squishy, smooshy, indirect, qualitative data. Interviews and long-winded answers to open-ended survey questions can provide context you just can’t get from pulling numbers out of a database, and can fill in those gaps of understanding left after a document review.

But how do you capture that in easily digestible picture form?

Jennifer Lyons, in a follow up guest post for Evergreen Data, breaks down one way to present qualitative data. Read more here!

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Stephanie Evergreen Reblog: How dataviz can unintentionally perpetuate inequality (3 part series)

Maps seem to be especially prone to misrepresenting people in disadvantaged situations, particularly as we get ever closer to being able to pinpoint individuals’ locations geographically. Vidhya shared with me a map of concentrated poverty in Minnesota (a factor extremely comingled with being a person of color), where individual participants were marked by red dots. When presented to the actual participants living in these areas, they were not stoked. Instead, they felt like they were perceived as a threat, and the little pixels made them look almost like an infestation on an otherwise subtly colored map.

In this Stephanie Evergreen series, she explores some of the missteps that researchers and presenters of data can make when attempting to quantify the human experience. Visual presentation influences how data is perceived and understood. It is very important that we as collectors and presenters of data consider how it may (or may not) come across, particularly to communities involved and represented in the research.


An approximation of the offending map



The same information, presented in a way that “aggregates the issue rather than identifying individuals”

Part 1

Part 2

Part 3

We as auditors and eager life-long learners sometimes tackle complex issues of social significance. The last thing we want is the wrong graphic or stock photo to undermine our findings or the legitimacy of our work. This means approaching the presentation of those findings (in the report or otherwise) with the same care and deliberation with which we approach other aspects of the audit process, and creating an end product that successfully communicates our intended message.

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Stephanie Evergreen Reblog: Two alternatives to using a second Y-axis

Almost as often as I see a pie chart with a hundred tiny slivers, I see line graphs using two y-axes. And it is just as bad.

Is a chart that looks like this:

secondaryaxisoriginal…really so bad?

While “bad” is perhaps a strong word for using 2 Y-axes, Stephanie Evergreen shares some alternatives in this post that may do away with the resulting confusion. Follow the link, or check out some options below.





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Stephanie Evergreen Reblog: Qualitative chart chooser

The reality is, most people are never going to be excited to read your text heavy 50-page report with no visuals. This is where data visualization can come in handy. Visualization is a great tool to get people interested and engaged with your story. The problem is, many of the qualitative visualizations I see are reports with endless callout quotes or ugly charts that were spit out of data analysis software. We can do better than this.

Stephanie Evergreen and Jennifer Lyons explore the possibilities of charting tricky and complex qualitative data in this blog post. Read more here, and explore some of the chart options below.


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Stephanie Evergreen ReBlog: Timelines, 4 Ways


The least helpful timelines I’ve ever seen are these:


where time is basically bulleted, as if each of these intervals is equidistant and as if a bunch of text is the best way to communicate something inherently not based in narrative. You are basically saying, this journey is going to be really confusing because I’m not willing to help you see what’s really going to happen when. Buckle up! And that’s probably not the vibe you’re trying to give off.

Read more about interesting alternatives to developing timelines (useful in reports! useful in project management! useful, all around!) over at Stephanie Evergreen’s humorous and enlightening blog.

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