Communicating Data is About Handling Egos and Emotions

Behind every furrowed brow and annoying question about your slide is someone’s ego getting dinged. People don’t like their egos dinged.

This tiny bit of emotional intelligence will give you massive insight into how to handle tough data communication scenarios we all find ourselves in at one point or another. I used to take disruptive people personally, like I had failed, until I got some confidence that my visuals and my content were, in fact, super helpful. That confidence (that comes with experience) is what helped me see disruptive people in their own light.

I had one the other day. Someone in my workshop was being a bit difficult and I handled it with some humor (more on that below) and put the question out to Twitter to see how others handle those situations. Soti’s compassionate way of handling someone disruptive revealed – BINGO – there were some bruised egos and emotions brewing.

Let’s walk through some common data communication scenarios, take a peek at what’s really going on with people’s egos and emotions (assuming your data, graphs, and slides totally rock), and think through an appropriate response. I’m going to offer multiple responses so you can pick what feels comfortable to you, and knowing that the appropriate response will vary depending on the power dynamics of the situation.

The Behavior Exhibited

Someone doesn’t like your graph and is dismissing you by questioning how you collected and analyzed the data.

The Emotional Ego Underneath

They don’t like the numbers the graph is displaying because they aren’t what this person hoped. This person has a personal stake in how those numbers look and if performance isn’t good, there may be consequences. Will I get in trouble for these numbers? How will I explain this to my boss? Could I get fired? Who would hire me during a pandemic? (People go down the emotional rabbit hole real fast.)

The Insightful Response

You can be prepared with a detailed description of your methods and analysis and sometimes that works. But it is trying to logic your way through people’s emotions. If your documentation isn’t cutting it, that’s how you know you have an emotional issue. Test the waters quickly with “I’m happy to send you the detailed documentation that back up how solid our methods are, given the budget and parameters we have.” If the frown is still there, try to distribute the burden with “We all want these numbers to look better than they do.” Then present it as an opportunity: “Thankfully, we have caught this issue now, so that we have the chance to turn this around before it gets worse. Let’s problem solve together.” Empathy is usually the best response to try, the first time someone is disruptive.

The Behavior Exhibited

Someone doesn’t like your graph and is making that clear by loudly proclaiming “I don’t understand this!” and “I can’t even read this!” and, even after some patient explanation, “I just don’t get it.”

The Emotional Ego Underneath

They don’t like your graph because they aren’t familiar with the graph type. They don’t know how to make that chart themselves. They don’t feel they will be able to learn to make something so intimidatingly cool. Their skill set is becoming obsolete. Can they make it to retirement without having to go back to school?

The Insightful Response

Help them feel like they are not alone, left out to dry: “Many people who are accustomed to the small selection of default charts in Excel don’t like this one at first glance…” then add in something that addresses the underlying issue: “… and that is often because they don’t know yet how to make it.” Then some reassurance: “I made this one right inside Excel and it is surprisingly easy. I’ll show you and once you know how to make it, you’ll warm up to it more.”

The Behavior Exhibited

Someone doesn’t like your chart type because “my boss won’t let me get away with that.”

The Emotional Ego Underneath

If I upset my boss, I could get fired. (So much of our ego response is based in threats to our underlying sense of security. Can you see that in these emotional ego thought patterns?)

The Insightful Response

In my case, I can usually reassure them that their boss, in fact, hired me to come in as the consultant and make these changes, so the green light has been lit. If there is still some hesitancy, I ask: “Even if you couldn’t pull off this entire visual revolution overnight, can you get away with implementing some small aspect of it? Maybe just the great title or a switch in colors? If you can make tiny changes every few months, in a year you’ll get there. Sometimes it is an evolution, not a revolution.” Make it feel do-able.

The Behavior Exhibited

Someone doesn’t like your graph and is making that clear by proclaiming “That just isn’t the way we do it. People won’t understand this change.”

The Emotional Ego Underneath

What they are really saying here is that the proposed change deviates from the norms. And disruption is bad, people will get upset about change and I’ll have a lot of upset people on my hands. (Now the original person is bringing in the emotions and egos of other people too, see how this works?)

The Insightful Response

This person needs to see that change is not necessarily bad just because it is change. Pitch a response that is contained in a halo: “The hero of every story becomes the hero because they made a change to the status quo.” Nice, right? Address concerns: “Some people might drag their feet on this but other people will be by your side and we’ll grow as a team through any discomfort.” Then appeal to some other emotions: “Taking people through change is what leaders do.”

The Behavior Exhibited

Someone doesn’t like your graph and is making that clear by picking at some minor aspect of your slide, distracting the conversation from your main point.

The Emotional Ego Underneath

They actually really like your slides and are so impressed by the design they are scrutinizing how you made and didn’t even realize that the conversation was elsewhere. You made good design look so easy they think they can do better than you so they are taking mental (and verbal) notes about what they’d tweak.

The Insightful Response

Humor works best here (at least for me). Laughter is the expression of emotion, so make a joke like “That’s a fantastic observation! I’ll have you make all of my slides next time.”

Another big piece of protecting egos and emotions is how we deliver the insightful responses. For some people, they’ll need to be addressed privately, pulled to the side during a break in the agenda or debriefed after the fact, like Soti did. For other people, the more gregarious or intentionally incompetent crowd, humor and confidence go a long way.

When I first became a parent, someone gave me some sage advice. When your fresh baby is screaming and red-faced, it can look like anger but it is probably one of a small number of underlying conditions: hunger, tired, lonely, or sick. What’s showing up on the surface is rarely the full story.

The more we can train ourselves to hear the emotional need behind the “difficulty,” the better we will be at communicating with data AND bringing everyone with us.

Comparing Two Survey Questions in One Graph

You’ve asked employees to rate a bunch of different aspects of their job. You want to know if they think that aspect is important AND how satisfied they are with that aspect of their job. So, naturally, you make two individual questions with response options like Not at all Important to Very Important and Not at all Satisfied to Very Satisfied. I would probably do the same thing.

But then you’ve got to show the data and, importantly, how those two variables – Importance and Satisfaction – relate to each other.

A member of the Evergreen Data Visualization Academy sent me this predicament and her first shot at a visual: the two stacked bar charts below.

It’s going to take a lot of mental gymnastics to flip back and forth between these two graphs and pull out meaningful insights about the relationship between the two variables. This Academy member knew this could be better but just didn’t know where to take it next, so she submitted the question to our monthly Office Hours session, where I fix graphs live on the air.

Like most self reported surveys on satisfaction, the data skew positive. Given that, I recommend we drop the negative responses and focus on comparing the positive, which we can aggregate into one big happy bucket.

Once we are working with just one set of percentages per variable, we can better compare them with something like a back to back chart.

With one variable ordered greatest to least, we would hope to see some kind of pattern in the other variable. No such luck. This actually tells us that there isn’t a strong relationship between Importance and Satisfaction. And that’s a big insight!

If you or your clients want to dig for more meaning, you could keep graphing. Hopefully when I started talking about “relationships between two variables,” you started thinking about correlation and scatterplots.

Scatterplots would let us see individual data points that we might want to explore further, highlighted here in bright blue. We would finish this graph off with an awesome title that directly speaks to the insights found in the data.

Either of these redesigns will help this Academy member launch conversations with her clients so they can make decisions and take actions. And they’ll probably keep hiring her because she helped them get there.

Academy members gets these redesigned visuals and this advice from me as a part of their membership in the Evergreen Data Visualization Academy. In addition to the dozens of video tutorials that show how to make graphs step-by-step, the research articles, the templates, the fun, the community, she also gets ready access to my skills and knowledge.

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.

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

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row admin_label=”row” _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”3.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”]

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. 


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.

From the blog