Use Proximity for Better Data Storytelling

Recent clients, Planned Parenthood Federation of America, faced a design problem so common they didn’t even know it was a problem. They were working on communicating some data from a program that aims to educate on healthy sexuality and reproductive health. They surveyed participants at the start and end of the program and had some great results to share. But they showed the data in a way that can be hard to connect.

Each of the two bullet points in the section below relates to one of the lines in the line graph. But their separation by both space and color doesn’t make it obvious that these pieces belong together and makes it more likely that their sweet results will be skipped by most viewers.

The rule of proximity suggests that people will see things as related if they are close to each other. In my remake, I eliminated the separation by using the chart title space as the main heading and I leveraged data labels as spaces to hold their main findings.

By moving the key findings down in to the graph, we unite the text and the visual and increase our chances that readers will more readily understand the results of this program. Here’s how I did it.

First of all, I combined the data you would traditionally see in a spreadsheet into a single sentence, using Excel’s CONCATENATE formula.

See the formula, up in the formula bar? It is combining the contents in cell A2 (which would have been a legend) with the words “rose by” with the contents in cell D2 (formatted as a %) with the word “from” with the contents in cell D2 (formatted as a %) with a period. It takes a lot of quotation marks to make this happen but it isn’t complicated to figure out. The result is a full sentence that will change based off the data.

Then I added data labels in the graph and then tied one of them to my concatenated sentence.

To make the tie, click on one of the labels. Then go to the formula bar and type an equals sign. Then go click in the cell that has the concatenated sentence. Hit enter. Boom. Now the sentence fills the data label.

And it takes that data label to a whole new level of communicating our findings. Labels can do more than just hold a simple number. And because we hijacked a data label to hold this finding, the finding sentence will be tied to that point of the graph. This means that next year, when the data changes, both the label’s position and the contents of it will automatically change as well.

Pretty cool, right? Anything I can do to make work easier for comrades like Planned Parenthood – and you.

Makeover: Health Data Edition

By far, my favorite emails to receive are from clients bragging about the beauty and impact of their work after our workshop. I just got one of these the other day from Becca Scranton at Arizona Department of Health Services. After thanking me for the workshop, she wrote, “Thanks to you, we reworked our 2016 Annual Report. We cut out walls of unnecessary text, reducing what was once a 56-page report to 14 pages (excluding appendices). This year we purchased a Data Academy license for one of my epidemiologists. I look forward to continuing to improve our data and hope to apply your methods to all of the work we do.”

HIGH FIVES, BECCA!

Let’s take a look at how far they’ve come.

Their old report was looking like this:

and this

with an executive summary like this

and even though this is reporting on super important data like STDs, they were pretty sure no one was getting a lot out of their report.

Fast forward through our workshop to the next reporting cycle. Just one year later, their report looks like this

and this

with an executive summary like this

YES PLEASE! Awesome work.

I asked Becca to share some of their behind the scenes secrets.

Q: What software did you use to make this?
A: Everything was done in Word and Excel. We also used Flaticon for some of the icons, others were created using shapes in word.

Q: How long did the report process take?
A: The report took about 2-3 months to write and the rest of the time was spent waiting for data to be finalized and approvals from management and the media team. We ran some preliminary numbers over the summer, but we really didn’t start the report until October. We also had to start and stop a couple of times because there were some bad habits that needed breaking. By early December, we were able to submit the report to our Manager and then to the media team for approval.

Q: The report is for 2016 but it is 2018. Is the delay due to getting the data, analyzing it, reporting it, something else, or a combination of those things?
A: Our annual data is ‘frozen’ during the last week of April and CDC doesn’t release their numbers until the end of September. During this time we have several other reports that take priority. We usually start working on the annual report in October and try to get it out by the end of November, but we almost always publish our final report in January/February. Knowing what we know now, I’m hoping to get the 2017 report out by early December.

Q: How did you convince the right people that this change in the visual nature of the report was necessary?
A: We were lucky to have the support of my boss and upper management. They really understand that we needed to make a change.

Q: What kind of reaction have you gotten so far to this report? Anything related to its high impact visuals?
A: We’ve gotten a lot of positive reactions. Our County partners have started coming to us for questions about data visualization, the HIV team is ‘competing’ with us to improve their annual report, and the STD Team (as well as other groups at ADHS) were recognized for their contributions toward data visualization by our Director just this week. We also seem to be getting better questions from reporters about our data. Earlier this week, we had a reporter submit questions by email. She actually referred to the data displayed on our STD data dashboards!

Folks, read those impacts again. HELL YES!

Check out the full report here. For kicks, check out their old report. Even the cover design is a major improvement.

Read more about our workshops for data analysts so you can experience the same communication shift as Becca’s team.

Academy membership is closed for enrollment until this Fall but you can learn more about what’s inside and sign up to get early notification.

Launch That Consulting Practice

A while back, I announced that I was launching a mentoring program for women who are seeking to catapult their new business into the real world. I said I would take 4 mentees for a year. I had 70 applicants. I spent days reading through each woman’s essay, learning about her, researching her and her business, and giving specific and tailored feedback even to those I didn’t select for the program.

Even though each applicant and her business and her situation were unique, I found myself giving some of the same advice over and over again. If so many of them needed this advice, you probably do, too. So here ya go:

Stop doing pro bono work.

If you are struggling to get a roster of paid clients, you can not afford the time to do pro bono work.

I know, I know. It might lead to referrals, right? So do paid clients.

Pro bono work might generate something you can include in your portfolio, right? So do paid clients. And so do fake clients! If you really need material for a portfolio, create “model” or “sample” pieces that reflect what you do for *paid* clients – and it’ll take you far less time than that pro bono work.

When you have a full deck of paying clients, I strongly encourage some volunteerism. Go ahead and give back. Of course! But until that time, reserve your schedule for the hard work of building your business.

Just go.

Pick a name. It almost doesn’t matter what you choose when you are starting out. You can always change your mind later. Is your name Karen Oberhill? How about Oberhill Consulting. Boom. Done. File the LLC paperwork and go.

Too many of the women I saw were full of beautiful ideas that could change the world but were stuck at an early and easy step, like choosing a business name. The world is waiting for what you have to offer, so get out of your own way.

Lack of a singular business focus was a common obstacle and that’s legit. I went through this course by Seth Godin ages ago and it helped me refine how I framed my work.

Some women said they didn’t know the process of starting a business. So here it is:

  1. Pick a name (see above)
  2. File LLC paperwork. I used LegalZoom back in the day but recently had a lawyer chastise me for this, though I think that’s what lawyers are supposed to say.
  3. Buy a domain name and build a website. Squarespace makes website building so easy, you can do it in an afternoon. Potential clients need a place to go to learn more about you and send what they learn in an email to their boss. So website usually comes before networking. Which leads me to…

Get your name out there.

Where are your potential clients? Instagram? Twitter? Wherever they are, go there and talk about your business. Consistently – which means at least twice a week.

Target specific clients you’d love to work with. Then go to LinkedIn (Yes, it finally has a purpose) and see who you know that knows someone who works at a place on your dream list and ask for an introduction. That’s how it works.

So these three ideas are the basics to give you the space and direction you need to get your business running. However, the real issue that I think is at the core of all of this is a lack of confidence. Some imposter syndrome. And it drives this mentality that everything has to be perfect before you launch or no one will take you seriously. Listen, nothing is going to be perfect. Every successful consultant is constantly tweaking her website and periodically rebranding. So aim for “Good Enough For Now” instead of “Perfect.” Confidence comes as you get paid fairly from clients you respect. Which means you just have to go get them.

Spectrum Displays

guest post by Jenny Lyons

When presenting qualitative data, we want to balance visuals that are broad enough to display the full set of data but are also visualized in a way that allows viewers to pull stories from the data. This is a hard balance to strike, and a spectrum display can do so very well. In my first qualitative blog post, I introduced this chart type and gave a real-world example. A spectrum display compares the relationship between qualitative cases and themes, using a simple, at-a-glance visual.

Let’s look at an example and break it down. This one was originally cited in Stuart Henderson’s article “Visualizing Qualitative Data in Evaluation Research” (a great article if you haven’t read it):

The spectrum display shows summarized data collected from open-ended interviews and observations of how 34 library computer users spent their time (Slone, 2009). The labels on the outside (P1 – P34) are different individual cases or people. The researchers were observing different activities like paying bills, searching, job, and email. Each ring in the spectrum display represents data for each activity. The kicker is that you must have a mutually exclusive variable, which, in this example, is time spent doing that activity. This is how the cases are ordered.

The benefit of using this visual is that it allows you to see data stories like: 1) Most people who used the computer for more than 30 minutes were doing job related things, and 2) Signing up and paying bills took less than 25 minutes for all participants but one. These data stories open the door to better understanding. This display can make those data come to life better than the typical qualitative reporting method of page after page of narrative text. Below is this same chart but employing other effective data visualization best practices like impactful titles, intentional use of color, and some de-clutter action.

There are two limitations to this graph type that I want to mention before we move along to making this. 1) The chart drills all our qualitative down into codes and symbols. It can be easy to lose the person behind the dots. 2) You need to have a mutually exclusive variable, which not all qualitative data sets have.

The reality is that there is no easy way to make this chart. It takes some creative thinking and PowerPoint hacks. Below are the steps that I take to make this in PowerPoint.

1. Make concentric circles for the number of activities or themes you are tracking (plus one). I inserted 5 circle shapes, with no fill. I made the difference in size decrease by 1 inch. Make sure the smallest circle has a white fill.

2. Then insert a smaller circle for the bull’s eye center.

3. We need enough rows for each participant, equidistant around the circle. Insert 34 horizontal lines that are the same width as the circle diameter. I am inserting 34 lines because there are 34 people that were a part of the research. Now, divide 180 by the number of people, in my case 34. I get 5.29. We need each line to the 5.29 degrees different from each other. The problem is PowerPoint does not let you enter decimals for degrees, only whole numbers. Therefore, we are rounding down to 5 degrees for most. To fill up the 180 degrees, 10 lines will have a difference of 6 degrees and the rest will have a difference of 5. This will not be 100% perfect but it is as close as we can get.  To change the degrees on a line, right click and go to Size and Position. Then, you can edit the degrees. Each line should add 5 degrees beyond the first line. The first line should have 0 degrees and be completely horizontal. After you get all the lines in there, align them to the middle and center. It should look like this.

4. Now, you will align these lines center and middle to the circles. You will need to bring the grouped circles to the front, then group. It should look like this.

5. Now, we need to make each of our 4 mutually exclusive category lines darker grey and bring them to the front. You might need to play with ungrouping and arrangement to get it right. This is what it should look like.

6. This is the basic structure and all you need to do is delete the bottom half and insert text boxes and dots. To delete the bottom half, make sure everything is grouped. Copy and paste it as an image, then “cut” the bottom half of the image off.

7. Let’s insert text boxes for all the text.

8. Now, add color coded activities in text boxes and corresponding dots.

9. The last step is to add a rock star title that emphasizes my two takeaway points.

In 9 steps, you are now on your way to making some awesome spectrum display charts! This chart gives you the power of presenting your whole data set while still displaying overall themes and stories in the data.

Check out other methods for visualizing qualitative data in our growing Collection.

We recently invented a process to make spectrum displays right inside Excel. And Tableau. And R. Come find the instructions in our Academy or Graph Guides programs.

Learn in the Academy!

You can find step-by-step instructions on how to make 60+ awesome visuals in my Evergreen Data Visualization Academy.

Video tutorials, worksheets, templates, fun, and a big-hearted super-supportive community. Learn Excel, Tableau, R or all three. Come join us.

Enrollment opens to a limited number of students only twice a year. Our next enrollment window opens April 1. Get on the wait list for access a week earlier than everyone else!

Master Dataviz with Graph Guides!

Our newest program, Graph Guides, is a custom-built, year-long sprint through 50 Academy tutorials.

When you enroll, we’ll assess your current data viz skill set, build you a customized learning path, and hold your hand as you blaze your way to new talents.

We open enrollment to 12 students at a time and only twice a year. Get on the waitlist for early access to our next enrollment window.

How to Communicate Odds Ratios

Odds ratios are tricky. It isn’t actually all that hard to come up with some decent ways to visualize them. The tricky part is interpreting the results in a way that makes sense to average readers. How do you put the phrase “odds ratio” into a clear and easily interpreted sentence?

The Kansas Department of Health and Environment partnered with me to solve just such a problem. They had important data on the odds that people with histories of sexual violence will also have other health conditions. We went back and forth in our discussions about how to frame the results of their analysis. They wanted to say things like “5.8 greater odds,” which doesn’t have practical meaning to most of us. We had to work hard to balance out what is digestible to the public and most precise to the scientists responsible for data collection and analysis. We discussed (and I still ultimately favored) the option of framing it in terms of “likelihood,” as in “Men who have experienced sexual violence are 5.8 times more likely to feel depressed,” which I think is a more familiar concept the average readers. Ultimately, my clients felt the best balance was struck by using wording around “greater odds.”

So ultimately, we decided to visualize the odds ratios as a simple bar graph, which textboxes that interpret the top bar of data.

I get a handful of questions about how to visualize odds ratios every year and I usually tell those people what I just told you. Researchers Leon Gilman and Gerald Davis from University of Wisconsin – Milwaukee were behind one of those emails. They were trying to graph odds ratios related to race and disciplinary suspensions within a school district but said that staff in the school district found “odds ratio” to be too abstract and that they particularly had trouble interpreting odds ratios that were below 1. They ran with the idea of “likelihood” and produced this visual:

and they took it even further by completely rephrasing the discussion in terms of equality:

and, wow, is that a pretty powerful statement.

We help folks sort out these issues on their own projects, propelling them to data rockstar status, in our Academy and Graph Guides programs.


Learn in the Academy!

You can find step-by-step instructions on how to make 60+ awesome visuals in my Evergreen Data Visualization Academy.

Video tutorials, worksheets, templates, fun, and a big-hearted super-supportive community. Learn Excel, Tableau, R or all three. Come join us.

Enrollment opens to a limited number of students only twice a year. Our next enrollment window opens April 1. Get on the wait list for access a week earlier than everyone else!

Master Dataviz with Graph Guides!

Our newest program, Graph Guides, is a custom-built, year-long sprint through 50 Academy tutorials.

When you enroll, we’ll assess your current data viz skill set, build you a customized learning path, and hold your hand as you blaze your way to new talents.

We open enrollment to 12 students at a time and only twice a year. Get on the waitlist for early access to our next enrollment window.

Announcing The Interactive Data Visualization Checklist

If you’ve been anywhere near the world of graph making in the past several years, at some point someone probably sent you the Data Visualization Checklist, developed first in 2014 by me and Ann Emery.

We built the checklist based on the best available research I was seeing via my dissertation work and book writing and the best practices Ann and I knew to be effective from our design work with clients worldwide. It lists out specific guidelines in five areas: text, lines, color, arrangement, and overall – on how to best format a visual so that the data story is clear, regardless of the software used to build the visual. The checklist has been used in practice by thousands of people like you – graph builders, data vizards, chart lovers – in that time.

And while Ann and I piloted the checklist with a panel of evaluators, we never had it formally tested for statistical validity or reliability. Until now.

Sena Pierce Sanjines, a PhD student at the University of Hawaii, has just finished her dissertation, studying the Data Visualization Checklist. She interviewed people like you to understand their thought processes and whether they were interpreting the checkpoints in the way that Ann and I intended when we wrote them. This is a way to test validity. She then trained raters to use the checklist to rate graphs and looked at whether their ratings were consistent – as in, whether the checklist was accurately guiding people to the right rating. This is a way to test reliability. The results of Sena’s validity and reliability testing were so stellar that we decided it was time to materialize a long-term dream:

An Interactive Data Visualization Checklist

Upload your visual and the site will walk you through each checkpoint and help you assign a rating.

If any checkpoint is unclear, we have built in illustrative examples. If any rating is unclear, we have included some helpful details so you can discern the right score.

You’ll rate all 24 checkpoints in about 5 minutes or less. At the end, you’ll see your visual’s total score, along with a list of the checkpoints where you rocked it and places where you could improve.

If you aren’t feeling all that familiar with data visualization or how to use this checklist, we also made a short training you can learn from before you get started.

And if you want to read the details on Sena’s findings, we have those technical notes for you, too.

Many people use the checklist as a guidance tool while they are developing a new visual. If that’s the case for you, download a static copy of the checklist.

Others use the checklist as a way to assess completed visuals or works-in-progress to see what to fix before publication. If that’s you, try the interactive version.

Training others on data visualization? Use the interactive checklist as group discussion activity.

Deciding on a company data visualization style? Run a few of your recent visuals through the interactive checklist.

Need to convince your boss that data visualization could be improved at your company? Pop one of his visuals through the interactive checklist and post a print out of the results in the break room. I’m just kidding, that could get you fired. Pop one of your own visuals through the interactive checklist and email your results to your colleagues to kickstart some honest conversation.

Should You Make an Infographic Resume?

A family member works in IT at a large corporation. She recently forwarded me this resume they received for a job opening. Apparently, it had been passed around the interview committee with the subject line “Best/Worst Resume Ever?”.

I personally liked this one for the fact that I know how much attention to detail is required to make something like this. It says a lot about this candidate’s persistence, design thinking, and eye for the little stuff.

I get questions all the time about whether people should make infographic resumes. If infographics are today’s marketing currency and a resume is essentially your professional marketing, why not?

Well, I think you have to play to the player.

My sense of the interview team’s view of this resume is that it showed off skills that this job wouldn’t require. No design needed in their IT department, I guess. The infographic format really made some things pop out, like the fact the candidate only gave themselves 2 stars for punctuality! Not the kind of thing you’d see on a more traditional resume. Favorite food seemed to be taking up a lot of real estate. Is a map of the United States really needed?

The things I thought were humorous or cute were total turn offs to the interview committee.

I put this question out there on Twitter as a poll. After 24 hours, the results were:

Elijah Meeks saw the early results, which looked super similar, and said:

So play to the player. If you are applying for a job that requires design, data visualization, creativity, or visuals, an infographic resume could really work. When I hired for my assistant position, I was definitely drawn to the resumes with some design eye. Show me (or Elijah) what you know!

If you are not applying for a job where design and visualization are explicitly in the job description (and that’s most data-related jobs, unfortunately), don’t do it, my friend. Another person commented:

I didn’t even know HR CV ingestion tools were a real thing, so if you are aiming to fill a position at a large corporation, especially think twice.

If you really want to show off your design skills but you need to go with a traditional resume format, here are two ideas: (1) Wait until you get hired, then wow them with your design skills. (2) Pull together a portfolio and include an easy link to it somewhere in your resume.

Announcing: The Evergreen Mentoring Program

I can’t stop thinking about Harvey Weinstein. And as soon as I think of him, I remember all of the times I’ve been intimidated, harassed, undermined, or overlooked because I am a woman. With a deep breath, here are a couple of those experiences.

When I was a graduate student, another (married, Saudi Arabian, male) graduate student tried to pressure me into sleeping with him. It was preposterous and also gross and inappropriate. I told our mutual (male, American, white) advisor, who simply said “ok, thanks for letting me know.” The next day, when I saw my advisor, he looked me up and down and said “damn, look at you. I’d hit on you, too.” Nothing else was ever done. I was still expected to attend class and work on projects outside of class with the student and graduate under the advisor.

I was joining a podcast via Skype. My audio was connecting before my video was connecting. So I could hear the host and a guest discussing how hot I am. Then one of them said “oh, she’s joining.” And I was expected to have an equal and intellectual discussion with them at that point.

Post workshop but pre-getting-the-invoice-paid, I went to dinner with the head of the company who then invited me to his apartment to “see a view of the city.”

These are incredibly minor compared to what other women experience but I’m quite willing to bet that many woman are nodding their heads and thinking #metoo as they read. It sucks. It makes white women have to work twice as hard to get half as far and double that (or more) if you are a woman of color.

Men, circle up and take care of your own.

Women, I invite you to consider applying for the Evergreen Mentoring Program. Please read all the way through this description to see if this is right for you.

I am launching the Evergreen Mentoring Program to give women a safe, harassment-free zone to grow and develop a business under the guidance of someone who has done it well (um, me) among a very small group of other women.

Tomorrow is my birthday. I’ll be 38. I knew one day I’d want to turn to mentoring but I thought I needed to wait until I was in my 50s because who younger than that has anything to say? But people ask me for business advice so much and there are so many disgusting men that the need for mentoring is now. Let’s do this.

What it will involve

A 1-year commitment, starting March 1, 2018.

Regular communication (meaning daily, weekly) on Slack (I’ll show you how to set up) around a new topic each month. The exact agenda will be set based upon the needs and interests of the women selected for the program. Right now, the agenda includes: figuring out your focus, knowing what to charge, branding, marketing, all the dirty behind the scenes details of running a business, centering your ethics, choosing clients, and what to wear.

Really brutally honest conversation. You’ll need to be comfortable sharing private details like your hourly rate, for example. Likewise, strict confidentiality is absolutely non-negotiable.

Quarterly virtual group conversations on Skype. I don’t have time to waste and neither do you so it won’t be a bunch of chit-chat on Skype, it’ll be critical check-ins where we discuss recent monthly topics, how you are progressing in these areas, and how business-building is going.

A $20 per month financial commitment. This isn’t so you pay my bills. This is so you have a little skin in the game and are more likely to make the commitment to participate regularly.

Scripts, email templates, and other forms of support to set you up for success (based off of the very same things my mentors gave to me).

Who should apply

You identify as female and are in the early stages of starting a business. You should have more than a dream of starting a business. You should be on the ground, running it, or ready to do within the very near future. It doesn’t have to be your full time job.

You should be interested in learning how to run a successful business. I WILL NOT teach you how to do data visualization. That’s not what this is about at all. Your business does not have to be related to data visualization whatsoever. You do not necessarily have to be a running the business by yourself. This does not have to be your first career. I don’t care how old you are.

You can commit to regularly asking questions, doing a bit of homework, and responding to others. Perhaps up to 30 minutes a week.

How to apply

Send me an email, in which you tell me:

A little about you, your background, your identity

The stage of your business (there are not hard definitions around this, so just describe where you’re at)

Why you want to be a part of this

That you can commit to the time and financial expense I’m laying out here

Email it all to me by February 14.

Then what

I’ll select 4 women by February 28. Everyone will get a reply from me no matter what.

The 5 of us dive in on March 1.

With gratitude for the mentors who have come before me and with hope that we can build a better world,

Stephanie

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

[Stephanie’s Note: Social justice and reducing inequality are core values at Evergreen Data. Heather Krause at Datassist shares those values. She is one of the leading voices in discussions around inequality in data analysis and visualization.]

By Heather Krause

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. And that means that, if our goal is equality, feminist data visualization is a very real thing. One we can’t do without.

The God Trick

Objectivity is important in science. But as we’ve said, data is rarely objective. Donna Haraway talks about “the God Trick” —the (mistaken) idea that we can look at data with an unbiased “view from nowhere.”

“(Feminists) need the power of modern critical theories of how meanings and bodies get made, not in order to deny meanings and bodies, but in order to build meanings and bodies that have a chance for life.”

Donna Haraway, Situated Knowledges

Still, if we are careful to use data viz best practices, we should be able to convey what we know with a modicum of objectivity, right?

Not quite.

How We Interpret Data Visualization

Kennedy Elliot of the Washington Post spoke at last year’s the OpenViz Conference, where she discussed whether data visualization is a science or a language. In her talk, she cited 37 of the most influential studies on viz and how people interpret graphics — many of which are the basis for best practices today.

But only two of those studies included any participants outside of North America. All of them were conducted in English. The vast majority used students at expensive colleges as their subjects.

Do you see the problem?

All of those studies were run — and participated in — by people likely to share a common worldview. So chances are the best practices they produced only apply if you (or your audience) share that context.

Different Contexts Need Different Viz

Data visualization needs to reflect the culture that it lives in. This includes the culture the data came from as well as the culture of the intended audience. When designing data, a few of the many dimensions you need to think about include power distance, collectivism/individualism, and relationship to time.

A few years ago, we worked with a team in Bangladesh on a project aimed at helping low-income dairy farmers improve their productivity. But problems arose when we realized many of the women we were working with were illiterate, and could not understand the graphs we showed them.

We did extensive RCT research on how different cultures view data. Our discovery? Cultural context matters a lot when it comes to data viz.

These two graphs display the same data in different ways.

While the women could not read the chart on the left at all, they understood the chart on the right clearly. When we talked with the women about why they preferred the chart on the right they were quite clear that their ideas about time were much more closely related to cycles than lines. The less linear interpretation of time was more culturally relevant to the people who were both producing and interpreting this data.

This graph looks at aboriginal education over a brief view of time

We received the opportunity to partner with Aboriginal groups in Canada to do some data work and it was this experience that educated us on the cultural importance of power distance. We looked at a traditional national statistics bureau style visualization of education over time, spanning the years 1996 through 2011. To many, this seems like a long time. Charts like this often reflect the length of time “comparable” data is available. Up to this point, I had usually assumed that this was enough.

This graph looks at aboriginal education over a broader and more culturally appropriate view of time

When we dug deeper into the historical data held within the groups that were being defined in the chart, however, this is the chart that emerged. This time period is more meaningful to the cultural setting. This time period also centers the power of data creation and ownership in the relevant culture.

The Solution: Feminist Data Visualization

Catherine D’Ignazio is a leader in feminist data viz. She suggests that feminist data visualization offers a more responsible solution:

Feminist standpoint theory would say that the issue is that all knowledge is socially situated and that the perspectives of oppressed groups including women, minorities and others are systematically excluded from “general” knowledge.

We simply can’t make data viz completely objective. But feminist data visualization can help us take ownership of the claims we make with data — rather than simply shrugging our shoulders and pointing at the numbers. D’Ignazio offers three key ways we can make viz design more inclusive:

Come up with new ways to depict the limitations of a visualization.

Data viz conveys the data we have. Ensuring it also communicates missing, excluded, or out of range data can provide valuable context. Designers should verify the data they visualize and make efforts to find out why missing data is not present.

Reference the material economy behind the data.

I’ve discussed the importance of this in posts on data biographies. Making clear how data was gathered, who collected it, who funded the collection, and why it was gathered provides critical insight.

Facilitate dissent.

We often present data viz as fact — or at very least, the determination of an expert. Allowing your audience to question the data or present alternate views ensures inclusivity and democracy.

Data collection, analysis, and visualization are not neutral. All we can do is be honest and ensure we are transparent about our choices and limitations. If you need help with feminist data visualization, the team at Datassist can help. Contact us today.

 

 

 

My 2017 Personal Annual Report

Every year at Evergreen Data continues to be the best year ever. When I was younger, I knew my dream job would include meaningful work, awesome people, data, writing, and travel. I just didn’t know at the time how to put all of those things together in one place. Thank you for helping me make it all come true.

In the next month, I’ll be launching a new way to help you make it all come true, too.

In the meantime, my look back on 2017 includes great workshops in both Alaska and Hawaii, way too much time on Twitter, and a growing Data Visualization Academy. I get a lot of questions about my work so I’ve posted some FAQs below.

Most parts of the report are click-able. And, as with past years, this template is yours to use. I constructed it in PowerPoint and embedded it here using OneDrive so you should be able to download a copy. Keep in mind that you likely won’t have my specialized fonts and that the graphs and map are images. But I hope this can give you a headstart on your own one page reporting.

Frequently Asked Questions:

Q. When can I get in the Academy? This is on my professional development plan for 2018.

A. We open enrollment twice a year, in Spring and Fall. Exact dates aren’t set just yet but those who are on the wait list will get advance notice and an opportunity to enroll early.

Q. Where do you get the most work?

A. There isn’t one particular industry. I work with everyone from small nonprofits to Fortune 500 to government at every level. But, looking at the map, work does tend to come from cities around the perimeter of the United States (kind of – Colorado has a coast line in this hex map). Looking back over the maps from previous annual reports, I see an opportunity to grow more visualization and intentional design skills in places I haven’t traditionally reached as much. I’ll be partnering with some great people to bring workshops to Chicago and Kansas City in 2018.

Q. That’s a lot of miles, girl.

A. I know! And I fell short of the highest tier of Delta frequent flyer status by just one or two flights. And then I laughed at myself because why is that important?

Q. How do you manage to write so much?

A. The process is different for everyone but for me it is a matter of scheduling time with my butt in my chair. I block out the time in my calendar like it was a project or a class. Then I tweet for 30 minutes and then I feel bad and start writing. HOWEVER, that time with butt in chair is productive because my mind is already so full of what to say because I’ve been thinking about my writing and coming up with ideas because I spend a lot of time not writing and not working so my brain is free. So get off your phone and go for a walk, eh?

Happy new year.

From the blog