So COVID Interrupted Your Data Collection…

Kristi is a researcher in the Truman School of Public Policy and a rockstar member of my Data Visualization Academy. She was working on a study of an after school sexual health program when COVID happened, knocking her data collection strategy to its knees and leaving her without much to report about even the short term metrics of the program. Which is what she was hired to do.

Kristi came to our monthly Academy Office Hours meeting asking “Now what do I do?” and yall, let me tell you, I think a lot of us are wondering the same thing right now.

Here’s what I told Kristi during Office Hours. You have four possible ways to handle interrupted data collection while still constructing some meaning from the data you do have.

Add Comparison Points

Everyone is under the same umbrella right now, which means their partial data collection can serve as a point of reference for you. Add some comparison groups to your own data to produce a fuller, more meaningful story.

This option is possible if you and your comparison groups have fairly regular data collection. If everyone is experiencing a partial year of data, those partial data points are still comparable.

Perhaps you don’t have regional sister organizations or a national average… but do you have similar organizations in your same space? While folks are traditionally reluctant to share program performance data between organizations, we are now all in the same boat, trying to make sense of the data that we have. Who else in town was running after school programs aimed at your same age group? Use this as an opportunity to construct a new, more cooperative relationship.

Add History

Fingers crossed, you’ve been in business longer than just this year. If so, you should have some historical data you can add to the picture.

When you can, cut last year’s data at the same point in the year as when pandemic hit, so that you are comparing apples to apples. And include those comparison groups, if you have them.

Estimate from Imputed Data

So far my suggestions leave out actual end of the year data. But you could potentially estimate what your year-end data would have been by looking at growth during the same time frame last year (and even the growth your comparisons had in the same time frame last year).

You’d have a hefty caveat of “if things stayed just as they were last year…” which, let’s be honest, is a big IF. But that’s ok. We’ve got to make the best of what we have to work with right now. If anyone should give you any flack about this, you are dealing with a jerk.

Say It Loud and Clear

If you have no good comparison groups, no solid historical data, and nothing to estimate from, you can’t draw much of a conclusion about what happened with your group this year using the data you have collected. But you can say that COVID happened. It interrupted everybody’s everything. So, just say that.

Maybe you can call up a few of the program participants and get some quotes from them about how they miss the program or wish it could move online or how they’ve been so busy taking care of essential workers in their family that they haven’t thought about the program at all. At this point, that’s going to tell you a lot more about your program than the other half of the data you wish you had.

Kristi’s question is exactly the sort of thing we love to help people work through in the Academy. When you join, you become part of a supportive community full of empathy and ideas that you can tap into as soon as that giant question mark (or exclamation point) pops into your head. You get to pick my brain (and the hundreds of other rockstars) any time AND learn exactly how to make the suggestions we give. This is how you grow.

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.

5 Shifts from Presentation to Webinar

Well, here we are. In the Fall. At a time when, back in Spring, we might have thought we’d be gathering in person again. Now that awesome presentation you planned to give has gone virtual and you’re on Zoom instead of standing in real life in front of a crowd of interested people. What does this mean for your PowerPoint slides? Here are 5 shifts, plus an extra bonus shift if you are preparing a poster and not a presentation.

Shift 1: While People are Waiting

Turn on some music. Folks will be wondering whether their speakers are working and looking to test this before your presentation begins. Use for software’s functionality will play some classic jazz in the minutes leading up to the beginning of your presentation. You will not get questions about whether sound has begun. But if you leave things silent you will get questions from your audience, even though you are not at your start time yet, about weather sound is working. Seriously, in my last webinar people were commenting in the chat box about how much they appreciated my taste in music. In case you’re curious I was playing Mingus.

Add any preparation tips to your home slide. This way people will see how to prepare for your talk as they are waiting for you to begin. If they need to download any files or gather any materials, let them do so before you get started.

Shift 2: Adjust Your Font Sizes

I have been getting this question from many people in almost every workshop . You prepared a slide show with large file so that people in the back of the room would be able to see your slides. Now everyone is looking at your content at arms length. So what should your font sizes look like? I answered this question over on my Instagram.

Shift 3: Meaningful Interactivity

The rule of thumb for live presentations had been to change up the pace of things every 10 minutes. So 10 minutes of lecture, and then some change. Ask a question, dive into an activity, tell a joke. In webinar land my experience is that 10 minutes is now too long. Add more interactivity, perhaps every 5 minutes. But it has to be meaningful. Don’t toss up a poll that is useless or doesn’t add to the learning. Direct people to go to the chat box and answer an important question or provide their thoughts and insights. Make it worthwhile to interact.

Shift 4: Longer Breaks

In my live workshops, we would take a break every 90 minutes for perhaps 10 or 15 minutes to let people stretch their legs and find the washroom. Now that we all have work from home, we must consider that many of our attendees will have other people around them, such as children who need snacks or a diaper changed, such as parents who need snacks or a diaper changed. If we want our attendees to be able to focus with us for the time that we are together we must allow them to be able to attend to their lives. These days at Evergreen Data, our standard is to provide al least a one-hour break after each 90 minute segment. This is also a good reminder that we never know what other people are going through and our default mode of operation should be absolute patience and grace.

Shift 5: Speaker Camera Only

It is my perhaps controversial opinion that attendees are only asked to keep their camera on because the speaker is insecure. As a speaker you have to get comfortable looking into your camera and talking to it as if it was a very responsive group of people vigorously nodding their heads at you. It feels unethical to me to require our attendees to keep their cameras on. We don’t know their situations. But as a speaker, if you have obligated yourself to give a presentation, you should make sure you can keep your camera on the whole time, so people can see the face behind the voice. I upgraded my own webcam when pandemic hit and I’m telling ya l look so much better now.

Bonus Shift: Twitter Posters

Before pandemic even began, Mike Morrison was leading a revolution around research posters. (Can I get an Amen?) Now that we are not convening in person to share our posters at conferences, Mike has adjusted his #betterposter agenda to fit the idea of sharing your poster on your social media channels. Mike even provides templates for you to create a poster suitable for Twitter. Get started by watching his video, it’ll change your life.

Great Charts Make Even Better Entrepreneurs

Meet Tamara Hamai. She’s the hardest working entrepreneur I know. Her primary business, Hamai Consulting, partners with non-profits in the family and education space to get them useful data. If you get to partner with her, you are lucky. She’s got a knack for running complex studies and explaining the results clearly. She also mentors other entrepreneurs and appears as an expert source on the news. Seriously. She’s awesome.

So why does she need to up her skills in graph-making? Because great charts make her an even better leader.

Tamara joined our Graph Guides program to take her skills even further. Because she knows that if she has clear charts that back up her well-articulated research findings, her clients will be set up to get more mileage from her work. In this short 10-minute interview, Tamara tells us how the investment in learning how to master data visualization makes her an even better entrepreneur.

Here is how Tamara had been sharing results with her education clients:

Tamara easily admits that it is a wall of text. Well-written, clear, insightful text! But still, not exactly easy for her client to use this to get more children into their program. Her clients would get more leverage if they had some graphs to share.

Tamara’s old graphs looked like this:

Tamara worked with her Graph Guide to identify better ways to showcase her data for her clients – ways that can support her awesome text-based storytelling skills. While Tamara is still in the early stages of the Graph Guides Program, she’s already generating much clearer visuals. Here is a sample from a recent client report for the Boys and Girls Club:

Tamara is using data visualization to talk about the data itself – in this case a dot plot to reflect response rates over time.

And, of course, she is using more appropriate charts – and more communicative design – to talk about results:

Slopegraph, color-coded to reinforce messaging around increases and decreases – I love it! And so will her clients. This is the sort of tool they’ll use for discussion points in internal meetings and maybe even with external partners. In other words, these graphs help Tamara’s clients do more with her insights.

And the more they do with her insights, the more people see Tamara’s great work and the more people want to work with her. When Tamara made the call to sign up for Graph Guides, she was enhancing her own skill set to be a better partner for her clients and she was creating enhanced marketing for her future clients, too.

We love being a part of Tamara’s professional growth and we want to do the same for you.

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.

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!

Data Viz Rockstars Change Conversations

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Audrey Juhasz has been in our Graph Guides Program for about 4 months. In that time, she has learned some super do-able and highly effective lessons that have totally changed the way she and her team are able to talk to each other. This data viz rockstar is changing the conversation.

Her nonprofit colleagues have been developing new insights and making different data-driven decisions because of Audrey’s growing skill set. And she still has 8 months of learning to go! Dang, that’s good. Let me show you some of her work.

From Frequency Charts to Beeswarms

I am so in love with this makeover.

Audrey explained, “The frequency chart had all the departments together, then all the departments wanted their own individual data.”

Girl, we have all been there.

Audrey continued, “Putting it into the beeswarm completely changes the story, and is SO much easier than 5 individual frequency charts. Next year, I’m planning to revamp the entire ’employee wellness’ report using the beeswarm and hope it will condense it from 40 pages to about 10.”

Amen to that! Shorter, more condensed, more insightful data visualizations make Audrey’s life easier and make all of the departmental folks happy.

Bump Charts that Create Buzz

Bump charts (which show change in rank) can get prettttty complicated. Audrey’s created a strong example here, capped by an insightful title.

Creating strong titles is both the easiest and hardest thing to do to your graph. Audrey explains the hard part:

“In some ways, the hardest part of this process is that it’s forced me to step up and draw conclusions for other people. I mean, the whole point of the title is for me to tell the audience what I think is important. I’ve always tried to be really objective, so making that leap has been really difficult.”

And in the Graph Guides Program, we don’t let you skip this part. We help you take the hard step of coming up with insights about your data. Why? Because then you get to the easy part: The efficient conversations and streamlined organizational practices.

Audrey reported back, “Just last week, the education department head and I sat down to look at what I’d put together for her year in review, and it was really nice to be able to say ‘what do these results mean to you’ and I was able to change the placeholder title to really tell the story of the data. Last year, it was like she was completely paralyzed by what I had put together, and this year each slide was just a conversation about the story behind the numbers. She didn’t even ask me to come to present to the board with her like she did last year.”

How Audrey Grows

We paired Audrey with Dr. Sheila Robinson, one of our Graph Guide data viz experts. We assessed some initial examples of Audrey’s work and looked at her hopes and wishes (she is learning R as a part of her Graph Guide program) and plotted out a set of 50 graph-building tutorials (in Excel and R) that Audrey would need to finish in a year.

Every week or so, Audrey and Sheila check in about Audrey’s growth and her latest graph-making. They talk about datasets she needs to graph and the chart types that can do it the most justice. Sheila verifies each of Audrey’s finished graphs, ticking upward to 50 by next April.

Sheila gives Audrey detailed, private feedback about her work and serves as an at-Audrey’s-fingertips consultant all year long. Audrey grows, her nonprofit increases their efficiency, and the people they serve ultimately benefit.

Enrollment in the Graph Guides Program is only open twice a year, to 12 students at a time. We keep the student-teacher ratio really small so you get the same kind of close coaching that Audrey gets. 

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Go Beyond “The Data Varied”

The quickest way to tell a story with your data is to use the title space to literally tell the story. Identify the insights you see in the data and write them out as a full sentence, framing the take-away ideas, sharing with your viewers what you know.

This step, as simple as it sounds, can be difficult for people who come from academia, who aren’t used to being allowed to generate clear headline-style insights. When we practice this step in my workshops and webinars, my well-intended but under-practiced audience members will take a moment with a few friends to discuss a graph. We are usually looking at their own data, so let’s examine this one, created by Overflow Data:

Usually participants will come back to me with an insight like “The data on belief in God varied.”

Uh, yeah.

Got anything more interesting than that?

How about “Midwestern female Democrats are more likely than the US average to believe but have doubts in God or not believe in God at all.” That’s an insight (one that could even be programmed to appear in the title space as different drop down menu options are selected). Or “The majority of midwestern female Democrats have some kind of relationship with a higher power.” See where I’m headed with this? Audiences are on the lookout for headlines that pack meaning.

Now, I can’t hold it against the folks in my workshops and webinars. They don’t own this Overflow Data graph. They are not the experts on it. They haven’t been up to their eyeballs in it for the last 3 months. I only gave them a few minutes to study this. It is no wonder they didn’t knock insights out of the park.

But when it comes to our own data, the stuff we have been swimming in, we are extremely well-positioned to have insights that go deeper than “the data varied.” In fact, I’m confident no one else will know your data better. Audiences are, indeed, coming to you because you are the most knowledgeable person on the data, so that they can learn your insights.

Go beyond “the data varied” and tell people the stories you see in your data.

10 Years of Blogging

Friends, I’ve been writing this blog for 10. Whole. Years. Actually, it is 10 years and 2 months. The exact date flew right by as I was busy pandemicking. This has been a long road.

The very first post I ever wrote, published May 3 in the before times of the year 2010, was on why you shouldn’t have a table of contents. (Hint: it means your report is too long.) It wasn’t terribly well-written. Didn’t even include pictures, yall.

Since then, I’ve published 263 posts. Holy crap, I didn’t even know I had that many ideas. Many evolved into sections of one of my books.

Here are some of my personal favorites from over the years, not necessarily because they are the most popular with my audience, but because they are posts that I point people to regularly, even today:

Looks like I most enjoy writing up ideas that bring delight and change lives. That’s really what it’s about, isn’t it?

Somewhere along the line, blogging stopped being fun. Yall almost lost me back there. Because my comments would get so full of mansplanations of my own blog content right back to me that I was spending my precious time replying with stuff like “yes, that’s exactly what I said in paragraph two” and it was dragging me down, making me dread publishing. So I broke the golden rule of Engage with Your Audience and I quit allowing comments and my blogging satisfaction significantly improved and that’s the only reason you are reading this today.

These days I engage with my readers when you all reply to my latest newsletter and share your own stories, frustrations, and triumphs (and thanks for focusing on social justice issues). You tell me how grateful you are for this totally no-cost, steady stream of (mostly) good ideas, because you haven’t been able to get into one of my workshops or someone “borrowed” your copy of one of my books and you never got it back or you are just starting to explore the world of data visualization and didn’t know it could be this cool or joyful.

Your joy is my joy, for we can only be joyful together (to lean on the words of Archbishop Desmond Tutu). Thank you from the bottom of my heart for walking with me. Let’s round the corner and see what’s next.

So What?

In the introduction to our dataviz workshop at a Fortune 50, the Chief Operating Officer told the room of his employees that he was looking forward to seeing their work improve as a result of what they learned with us. Because, he said, what they wanted to see on the slides in their decision-making meetings was “your claim and the evidence that supports your claim.” Can I get an amen?

He wants to see something like this:

I don’t think he is unique at all. In fact, I am of the belief that most of us are filtering what information we choose to consume by how well it answers “So What?”. What’s your point? Many, perhaps the majority, of data visualizations are not answering “So What?” for our intended audiences and are under-used as a result.

Let’s walk through some examples, improving as we go.

Example 1: That Table

You’ve seen tables like this throughout your life, I’m sure. Tables presented in this typical, research-y way are a struggle for many folks because there is no evident point, no clear answer to “So What?”. The title doesn’t provide insight. There many be interesting points contained in the table, but it takes insider knowledge of the study, of how research is conducted, and of the possible implications of the data presented here to be able to answer “So What?” and that, my friends, is a tall order.

Example 2: Tesla’s Range

We are improving in that now we have a visualization of the data. The chart itself is a fairly familiar type – line charts are ubiquitous (even though this isn’t change over time, meaning a line might not be the right option).

Can you answer “So What?” when you look at this? All lines are going down as we move to the right, so you might answer “So What?” with something like “As speed increases, miles decrease.” But there are 4 lines, so maybe the answer to “So What?” is in the fact that the purple line is lowest? Or the red line is the highest? I don’t know. It’s a guessing game.

The real kicker here is that the text preceding the chart acts as if data literacy is commonplace and that the point will be evident if you just LOOK at the chart. But the issue is that there are so many take-aways in this chart that even the data literate are stuck asking “So What?”. The title needs to tell people the answer.

Example 3: 5G vs. Corona Scatterplot

Check out that crystal-clear title: 5G doesn’t spread the coronavirus. Perfect! That answers “So What?” instantly. Excellent! But….

It still takes a LOT of data literacy skill to see how (or whether) that point is made evident in the chart itself. Scatterplots can be complicated for many viewers. We could actually take this as an opportunity to teach data literacy by adding some elements to the chart that would help readers interpret it, such as a second, comparison chart that would indicate what a strong correlation would need to look like. So, in this example, the title is on point but the graph needs a tweak in order to support that title.

Example 4: Britain’s Coal

This chart from the Guardian nails it. The title is a short, succinct point that quickly answers “So What?”. The graph shows the evidence to support that point by popping out the days at 0% coal with an eye-catching green. It works.

It works even though the chart type is novel. Instance charts are a fairly recent evolution, used to mark the extent of something at regular occurrences. Despite needing to learn some data literacy in how to read the chart if you’ve never seen it before, all of the elements (including the legend) in this visual are working together to answer “So What?”. And because this point is clear in the title, it makes learning how to read the chart easier. It teaches some data literacy.

The Problem with “By the Numbers” Infographics

My heart breaks every time I see an infographic called By the Numbers. It’s as if someone in leadership said “Let’s report ‘our numbers’ this year – and put it in one of those infographics.” Someone in Communications got on board because they believe infographics grab attention. And some poor designer was tasked with trying to make some unrelated random bullet points into something cohesive. We end up with data pukes like this:

This is just a table in fancy fonts and colors.

And tables are really difficult for our brains to process. We don’t do so well trying to make meaning from random numbers. It is too many disparate bits that haven’t been pulled together into anything cohesive. Which puts the burden of cohesion on our audience’s working memory. Working memory just isn’t that strong.

Cognitively, we can’t do much with these By the Numbers infographics.

Even if this eye candy makes someone stop scrolling through Facebook long enough to rest their gaze on a single square, there isn’t much there to hook into. 49 podcasts…. uh, ok……… is that a lot? A little? Should I be impressed? How many did you have last year? How many did your competitor have?

Isolated numbers lack context. Context is how we get meaning. And that’s the thing – people are meaning makers.

We get context by adding more data points. More data points means we need graphs. Let me say it again for the folks in the back: WE NEED GRAPHS.

Context typically comes in three methods: Comparison against your own historical performance, comparison to internal goals, and comparison to external benchmarks.

1. Comparison Against Your History

This is low-hanging fruit for NPR’s number of podcasts – just show us the growth in podcasts over time as a line chart. I bet the line goes up a lot! Easy!

2. Comparison to an Internal Goal

In this fancy table from CDC, they no doubt have internal goals that could be added for more context and a deeper (but still quick) communication. Oh, you trained 3,758 emergency responders? Ok. What was your goal or target for the year? Add that data as an overlapping bar (just one option) and then readers can make quicker meaning.

3. Comparison to an External Benchmark

In these Stats, by one of my favorite magazines Bust, I realize they don’t have a lot of real estate to work with. But there’s an opportunity for a tiny graph in each of these sections. 15 states passed workplace harassment laws. Should I be cheering? I need the context. And in the case, it is easy to add context because there is a natural external benchmark (ALL 50 STATES PLUS TERRITORIES). I’ll even take a pie chart of this data.

Look, I’ve been there. I totally over-relied on this data puke style of infographic when I was first getting started. It’s just that it’s now time to evolve.

Leadership, Communications, and Design all need to align around adding meaningful context because context is where the data stories thrive.

Shaky Data Viz Advice

The biggest a-ha moment that came from my dissertation was discovering what shaky ground we stand on in data visualization.

When my friends heard I was going to study data visualization, they filled my desk with books from Edward Tufte, Stephen Few, and even Garr Reynolds. I was thirsty for resources and references because this was back in 2009, my young reader, before data visualization was so firmly established as a field. So I went down the reference rabbit hole. Oh, you know what I mean. You get one reference and you look up their references, read those articles, look up THEIR references, and so on until you know everything there is to know on the topic. Let me tell you: I didn’t get far.

Tufte, Few, Reynolds, and others who could be considered the elders of our field don’t have much to support their declarations and assertions – the ones we hear echoed, almost taken as “common sense,” still today. They cite incredibly little actual research-based evidence to back up their claims. Tufte has 5 in his famous The Visual Display of Quantitative Information. The sources they do cite are shared – meaning, they cite the same people. Few cites Tufte. It’s cute until you realize the implications.

You might think that they are excused because we didn’t have any data visualization research back then. But we did. Sure, less of it, but it was there. And in places it wasn’t, perhaps they could cool down the aggressiveness of their “standards.”

And they could make reasonable assumptions based on research from related fields. That’s what I had to do for my dissertation. I combed through literature in survey design, user interface testing, graphic design, typography, presentations, and more.

The fact is our heroes and history stand on shaky ground. Even modern books on data visualization lack research-based references.

One of my mentors is infamous for saying that not everything needs a randomized controlled trial to prove it is true or good. He said, some things are so obviously good or true, they have “interocular validity” – the kind that hits you right between the eyeballs.

But my mentor has something in common with Tufte, Few, and Reynolds. He was a privileged white guy, so his view of what looks good to his eyeballs is not necessarily shared.

The problem with interocular validity is that it means: If I think it’s right, it’s right. This puts us into difficult situations, where we are left tossing opinions. And opinions conflict. The Branding team at Nike may have had the opinion that this chart is good…

… and you might think they have a green obsession. It is no longer just a difference of opinion when the deep research on color by Dr. Cynthia Brewer gives us evidence to the contrary. Research gives you a backbone.

Communications may have thought this Tesla chart looks right…

…even if you are the internal data person trying to argue that bar charts should start at 0%, especially in this case, where a truncated axis makes them look safer than reality. When you have research to back up your recommendations, it turns your shaky ground a little more solid.

Now, research conflicts, too. But at least actual data gives us something to stand on (and this is why all Academy tutorials come backed by research).

Let’s knock down a few other pillars of viz history.

Ever seen this classic backbone as to why we should visualize?

The oft-cited 60,000x justification sounds sexy but has no basis in research. The reference rabbit hole on this one leads to a memo produced by 3M’s Visual Systems Division. Convenient that the Visual Systems Division would make such a conclusion about visuals, huh?

And this classic viz, produced by John Snow, is heralded as an example of the power is visualization. You probably know the story – during the cholera epidemic people thought the disease was spread by air. Snow marked cholera deaths on a map – one black line per person – and noticed they circulated around a water pump on Broad Street so they just removed the handle from the pump and the epidemic was over. Hero.

In reality, the notion that cholera spread through the air was accepted thinking until after Snow’s death. This viz may have been one drop of evidence but it didn’t have the heroic impact we like to think.

Let us question the foundation of our work because what may look legit to your eyeballs could be full of myth and conjecture.

Evidence, even if it is still evolving, informs decision-making. Heck, that’s why many of us are interested in data visualization in the first place, right? So let us not accept anything less in who we refer to for our data visualization guidance.

This is why my books have references at the end of every chapter. I am not messing around. My books are a good starting point. To nerd out more, investigate IEEE and Multiple Views.

How to Not Host a “Manel”

After a dust up on Twitter that I’ll explain below, I got so many white dudes in my DMs who wanted me to personally educate them on how to host a more inclusive set of keynotes and panelists at their conference. So I’m writing this post, without compensation, this one time, to point you all in the right direction. In the future, pay people for their consultation.

This discussion is focused on conferences and their keynotes and featured panelists but it also applies to podcasts, radio shows, YouTube interview series’, and any other places where you are hosting guest speakers. While I’m speaking pretty directly to the white dudes who host platforms, these points apply to a lot of white folks who say they “don’t know what to do” about racism in America.

A “manel” is a panel of speakers that are exclusively men.

The Dust Up

I’ll be as succinct as possible because the details aren’t important – the same situation comes up on a regular basis. A conference had used Twitter to promote a set of their featured speakers who “define data visualization” and all four speakers on their poster were white men. The speakers aren’t important, this isn’t their fault.

When I pointed this out in a tweet, the conference organizers made some rookie mistakes that told me they weren’t taking this very seriously:

  1. They got defensive in the public forum of Twitter but sent women from their organizing team to privately DM and email me with a much more open-minded tone.
  2. They said they tried to include more diversity among their featured speakers, but multiple men from the panel told me privately that they’d accepted this year’s invite because they’d been previous speakers at the same conference – so it doesn’t look like the organizers were trying that hard to find new voices.
  3. When others jumped into the Twitter conversation to express their surprise and tag dozens of prominent women in data visualization, the organizers reached out to those of us who were tagged with the *exact same* canned invitation email. Barf.
  4. They tried to use the excuse that they are in a male-dominated field without understanding their own complicit role in perpetuating the male domination. With platforms come responsibility. (And yes, the pipeline is another piece of this, which is not this blog post, but for starters, read through #BlackInTheIvory).

How to Not Host a “Manel”

In my tweet thread, I said that invited speakers can stick an inclusion rider into their contracts. An inclusion rider stipulates that my role in the conference is contingent on the requirement that other featured speakers and panelists are proportionately representative of United States (or wherever you are) demographics, meaning approximately 50% women, 50% people of color, 20% people with disabilities and 5% LGBTQ. I can cancel at any time with no notice if this stipulation is not met. Inclusion riders are a way for people with privilege to open up space for those who have been traditionally overlooked.

But instead of making the speakers come to you as the conference organizers with an inclusion rider, make this a core commitment – in fact, make this a POLICY – of your conference. Note intersectionality but do not try to find one queer black woman with a disability to check all your boxes. Do not hold your panel unless you can meet these bare minimums.

Are you freaking out? Afraid you’ll never have a panel again?

We need to unpack this real quick.

If you fear that you can’t find a panel of speakers that proportionately match US demographics, it is probably because your conference organizing team does not proportionately match US demographics either. I keynote enough conferences to know that pulling one together takes a ton of work. Usually that work is volunteer. People who have the spare time to volunteer are the ones who have the most privilege.
Solution: Pay conference organizers for their time and talent. You’ll attract a wider variety of people willing to organize. Then sit in the back so your voice is not the loudest one at the table. When you see people of color, especially women of color, talked over or dismissed, use your voice to redirect attention to them.

But usually we have a group of mainly white men organizing the conference or the podcast interviews or whatever and they inevitably end up claiming “we want more diversity but ‘those people’ don’t apply.” Um, hello? Why are you making people apply?
Solution: INVITE featured panelists and pay them for their time and talent.

Where will all this money come from?
Solution: No one wants another totebag.

So you have to know who to invite.
Solution: You have to actively follow, be interested in, and learn from people who are different than you. You have to earn their trust and camaraderie. Do not “lazy google” and simply tweet out that you want to know who to follow. That’s the equivalent of asking to copy my homework – it is inauthentic and you don’t actually learn. I’m talking here about listening more than you tweet and reading conversations between people who don’t look like you without interjecting your own opinions. I’m talking about doing the work to actually get to know more people. This can take years of investment. You’ll be a better person and a better conference organizer as you grow.

This is how you move from concerns (on all sides) about tokenism to true inclusivity.

I once got pushback on my inclusion rider from a conference organizer that he “wouldn’t feel comfortable asking if potential speakers were LGBTQ.” Dude, you don’t have to ask when you just know people in dataviz who are LGBTQ because you are friends with them, because you admire their work, because you are just in the community and you got to know them over time. This dude has never once interacted with me on social.
Solution: He’s not the right person to be organizing the conference.

You’ll end up thinking about accessibility policies and generating enforceable codes of conduct and you’ll end up with a better conference for everyone involved. Hell, you can even follow the models that others are working toward if you just did a little homework. Check out this example, recommended by Steph Locke. I’d like them to go even further, but they are at least being clear about how they are trying.

So rather than tell me you think women don’t apply to be speakers at your conference because they “lack confidence” (excuse me, I have to go scream into a pillow), try creating a culture that they want to join.

If you are hosting on some kind of platform, this is the responsibility you carry. And if you can’t carry it, you shouldn’t be hosting. Who you feature gets elevated. Who you elevate can either perpetuate the status quo or can help create the society that people are marching for right now.

From the blog