The long, sad rabbit hole of politics, healthcare, and the Supreme Court: How I tried to draw a map and failed

Numbers are so, weird. They lack emotion, judgement, subjectivity. And yet they are reflections of ourselves. And we are so willing to manipulate them, fight over them, use them to control one another. They are the good and the bad in us. I know you know this.

But this week, I was reminded of this yet again, when I tried to count states and draw a map.

It was a really tough week. On Wednesday, the Supreme Court heard oral arguments on the King v. Burwell case. That’s a case that will decide whether the feds get to continue giving the subsidies that help people pay for healthcare in a whole bunch of states (Did you get the “a whole bunch” bit? Hold on to that). You can read about the case on Vox in a really good explainer here. Even Michael Cannon likes it (and he doesn’t like the ACA much), except for these parts.

Numbers really matter in this case, and I want to talk about them, but perhaps not in the way that you think. You might be thinking that I’m about to make the case that the people in the states at risk won’t be able to pay for health insurance if they lose subsidies, and that I’m about to show you those numbers, but I’m not. That’s my day job.

How many states would lose subsidies if the Supreme Court decides in favor of the plaintiffs in King v. Burwell?

I want to talk to you about numbers in a different way. Here they are:

  • 37
  • 34
  • two-thirds
  • three-dozen
  • “more than 30″

The numbers above illustrate some of the ways in which different sources (news outlets, pundits, policy experts, advocates, my mom… okay, not my mom… but I bet she’d have an opinion if I asked her) are reporting the number of states that could or would (yes, that distinction is important) lose subsidies if the Supreme Court decides to take them away.

I was trying to design an infographic to illustrate how many states were at risk of losing these subsidies. I figured on about 45 minutes to create a map and a bar chart. But I couldn’t. Because there wasn’t a number. Or rather, because there were many. And each number was more loaded than the rest.

How many states would lose subsidies if the Supreme Court decides in favor of the plaintiffs in King v. Burwell? It’s like the beginning of a “How many people does it take to screw in a lightbulb…” joke. Except there’s no punchline because it’s not funny—families get to lose their health insurance over this one.

So my straightforward question took me down a long, sad rabbit hole of politics, healthcare, and the Supreme Court.

Seriously, how do you count states?

To understand why these numbers matter, you need a basic understanding of how the healthcare exchanges are structured. Bear with me for four sentences. Under the ACA, each state plus the District gets to choose whether to set up its own health insurance exchange or to let the feds do it:

  • If the state sets up its own exchange, it’s called a state-based marketplace and there are 14 (including DC)
  • If the state lets the government do it, it’s called a federally facilitated marketplace and there are 27

If the above two categories were it we’d be all squared away, just a knock-down-drag out fight over people trying to take healthcare away from low- to- middle-income people. No numbers involved. But there’s a gray area with states that have partnerships—and not everyone agrees which of the above categories these gray states could fall into if the ruling goes down.

  • Some state exchanges are called partnerships (a state-partnership marketplace and there are 7 to be painfully precise). This is where the states and the feds divvy up the work of administering the exchange and the whole thing is run off the federal website—
  • There are also states that do all the heavy lifting of administration and just use as a technology platform. They basically have a state-based marketplace but happen to run it off the fed website—Oregon, Nevada, New Mexico. (If you are a glutton for wonk terms this is called a federally supported state-based marketplace and there are 3 of these states).

Kaiser has a nice wonky chart that lays it all out here.

Why is the number of states so important in King v. Burwell

Now, the lawsuit in King is basically an argument over which states with which types of exchanges get to give subsidies. The plaintiffs claim that the law intended for subsidies to be provided only to states that run their own exchanges (state-based marketplaces). That requires a definition: What, exactly, is a state that runs its own exchange? And a number: How many states do that?

Then there are those three “gray area” states in play (Oregon, Nevada, New Mexico) that make counting things more complicated: Do they/don’t they meet criteria for being state-based marketplaces? Depending on who you ask, they’re either at risk (see above) or doing just fine, thank-you-very-much (see above). (I know people that would run me out of town for even asking that question. To those people I say, “Yes. They are state-based marketplaces. But not everybody agrees with you. See above.”)

If you are interested in more of an explanation of the politics behind the three “gray area,” so was I and so was Charles Gaba, who is waaaaay smarter than me. His popular ACA blog sums up a longer Politico piece here. The title is as good as the answer: In which Politico reporters Racahan Pradhan & Brett Norman FINALLY answer a question I’ve been wondering for months… (and here’s the original Politico article).

And this matters, why?

Because depending on how the lawsuit goes, which state counts as a state-run marketplace affects who gets subsidies and who doesn’t, either through how the court defines it or how the rules define it after a decision gets handed down. I don’t have the brainpower or the will to begin to cover the arguments in this blog, so I won’t attempt to do so. But it matters. Just ask the people who won’t be able to afford health insurance once they lose their subsidies.

So we have a situation where prominent sources are reporting the numbers differently, and it’s confusing, and it’s political.

These numbers are being reported very differently by, um, everyone. Not just proponents and opponents of the law. Everyone.

  • Some count 13 states plus DC as having exchanges with “state-based marketplaces”, which means that they report 37 states at risk of losing subsidies.
  • Others count the 13 states plus DC and also what I call the three other “gray area” states (Oregon, Nevada, New Mexico).
  • Others use more nebulous characterizations (see below).

Here’s a sampling of the most common reporting that you’ve likely seen already:

So why can’t a poor designer simply catch a break and just draw a map?

Well, politics, obviously. That’s part of the point of this post. To shed light on just how murky things get for the people whose job it is to make things…er… clear.

If you’ve got a day job, drawing a map can mean visualizing—not data—but the political stance of your organization.

You’re caught in the cross-fire between your craft—show what you see/know—and the advocacy goals of your organization. Or, for that matter, the politics of the issue itself independent of your organization. Things don’t always align neatly.

Oh, by the way, I did draw the map for this blog post. Please enjoy it.

Map showing how some states are counted in King v. Burwell case

It’s complicated. But there’s another catch.

And can I just say one more thing? This is a shameless plug for my own outfit. If you really are interested in what will happen to the people who will lose their subsidies (I’m totally serious), please watch this video. Regardless of what “number” we ultimately settle on, not much good will come of it if these families lose their subsidies and can’t afford to pay for health insurance. You can hear them tell you about it in their own words. It’s pretty compelling and yes, we produced it.

The “art” of compromise: Is there room for compromise in designing data graphics?

In my last post, I discussed how expectations and perceptions of designers are as important to quality data visualizations as are more conventional resources, such as time, people and money. But there is also a flip side to this–there are times when, as designers, we may be faced with a choice to compromise on how we present data. The compromises we agree to–or reject–are as important to our field as anything else. (Kudos to me for resisting the urge to title this “drawing the line in infographics.”)

A friend related to me a recent conversation in which an art director who, when presented with a bar graph of extreme values (very high and very low), asked the designer to “fudge” the size of the smaller bars. (They were visible–not hairline–but too small to comfortably fit the values inside of them. Presumably the art director wanted to nudge them up so that the numbers would fit inside of the bars.) My initial reaction was er… not favorable. I felt like a mother bear protecting her cubs (the cubs, in this tortured analogy, are the data). I may have uttered a few choice words, even.

The ethics of compromise.

But, once I calmed myself down, it occurred to me that this might be something interesting to write about. I polled a few designer and non-designer friends. What do you think, I asked. Was this a bow to art or clarity? Was it an unintentional breach of ethics or a well-intentioned attempt to make information easier to understand? Was it goal-driven or just lack of creativity? Don’t jump on the art director just yet. This isn’t about the choice that person made (that’s the subject of another post). It simply reflects the reality that, as in other professions, we’ll all be asked to make choices that, to others, may appear to be inconsequential. We need to make sure we handle these choices intentionally and carefully.

Here’s what came to mind after my conversations with other designers.

Book-binding: an invisible art

Let’s think about the book-binding trade of back in the day. The men (mostly men, anyway) who bound books hundreds of years ago were tradesmen. They had a craft which they revered. They apprenticed and, as journeymen, they traveled from place to place, learning and honing their craft to become–eventually–book-binders. This is not unlike the path that many information designers take today.

For all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. If the thing didn’t fall apart in your hands, you were satisfied as a consumer.

I won’t bore you with the mechanics, but suffice to say that binding a book involved a lot of work, much of which was invisible to the eventual and subsequent owners. Once purchased, the book was read, perhaps the craftsmanship briefly admired, and then it was shelved or passed on, sometimes for generations (think of the family Bible). And yet, for all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. Again, not unlike the process of visualizing data, much of the effort and care involved in sewing pages into folios, hand-stitching the spine–remained largely unseen. If the thing didn’t fall apart in your hands you were satisfied. End of story. And yet, despite this invisibility, these bookbinders pursued their craft with diligence and and care nonetheless. How well or how poorly they plied their trade was not immediately evident, as these old books often outlived their makers. They had no immediate incentive to be unduly diligent. And yet, I like to think that most of them did not cut corners. Why? I’d say it was self-respect and public recognition of the importance of their craft. Maybe I’m over romanticizing books (I do collect them).

Our craft: Are we short-order cooks or visual content experts?

My point? This is an issue of the ethics of our craft. As designers, we need to ask ourselves: are we short-order cooks or visual content experts? Are we hacks or tradesmen/women? Is data visualization a craft or only a paycheck? Is data an obstacle to be overcome or a living boundary that, with each challenge, offers us the opportunity to learn, do better, and to empower our readers by bringing information to the surface in a manner that brings with it a new understanding? And while, from the perspective of the client (or, in this case, the art director) it may not always be apparent that the accommodations they ask us to make are not wise, it is–nonetheless–our responsibility to do the right thing, and bring others along. In this way, we advance the field forward and our professionalism as well.

And that’s the crux of this post.

Whatever your intentions, what is the effect of the small compromises that you make in being precise, transparent and correct in how you present data?

The more seasoned amongst you may shake your heads and think that these things are self-evident. But to those of you who are just starting out (be it as younger designers or managers in charge of new data viz projects), this may not be something you’ve thought much about. It may not even seem like much of a big deal to you.

Making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear.

What is the effect of compromise on the designer and the team?

So, what happens when a designer makes those compromises? When I asked a few designers, they all had one response in common: morale and self-esteem. Here’s the thing: making that one small edit will be invisible to everyone but you. It’s not like your readers will ask you to send them your Illustrator file so that they can measure pixels before they read further. Like the bookbinder who sewed thread onto page folios, no one but you will see the guts of your files. But making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear. And these things add up to the slow devolution of what was once a craftsman/woman (if I may be allowed to use such an archaic term) to a hack.

And what happens when an art director suggests those compromises? Well, you risk losing the respect of seasoned members of your team, that’s obvious. Worse, you risk creating an environment that is progressively sloppy. And while no one will catch the small compromises, they sure as hell will catch the big ones. Remember the infamous Fox piechart?

Other examples of altering data

It doesn’t stop with information designers, as I’m sure you know. Another designer who Photoshops medical imagery (for example, a CT scan or slides of cancer cells) told me about a doctor who, when preparing images of slides for a research publication, asked the designer to darken some areas to make them more visible (thus allowing him to better make his case). The designer balked–these aren’t just pictures, he told the doctor–they’re data.

And if you want a more mainstream example, how about the furor over the Time cover of OJ Simpson in 1994? Or, more recently (2008), the Hillary Clinton ad which featured then Presidential candidate Obama with arguably darker skin?

What is unacceptable compromise to one might be reasonable accommodation to another.

There may not be room to make the wrong compromises, but there is always opportunity for discussion.

No one is perfect. And each of the examples that I gave leave plenty of room for discussion. As a newspaper friend recently noted, some photographers are adamant about not retouching any photos they take–including not cutting subjects out of backgrounds. Others are not as rigid. And not all of the participants in my informal poll reacted with extreme horror at the thought of slightly lengthening bars. Some merely grimaced. But all agreed that if you’re going to tread on thin ice, you’d best aware of it. Another friend points out that he noticed a disconnect between his former employer (a newspaper) and his current one (a corporation). He’s doing the same work–designing information graphics. But whilst former journalist colleagues (having their own code of ethics) would never have asked him to fudge the appearance of data, he feels that–in his current role as a designer in the corporate world–his colleagues have a lesser understanding and appreciation of what asking this might mean.

This isn’t necessarily a bad thing–handled correctly, it can present an opportunity for education. But you have to be willing to put yourself out there–a place that not everyone (perhaps less experienced designers or as employees with less seniority) is comfortable occupying.

As designers, let us be keenly aware of how the small choices we make for ourselves can add up to large consequences for our profession. I’d love to hear more from you on this. Have you been place in similar situations? How did you handle them?

Infographics: Does time equal quality?

Does time equal quality in good infographics? Nope, not necessarily. I’ve been giving this a lot of thought lately and, in reading recent posts by Seth Godin and Alberto Cairo, it’s interesting to see how each touches upon what I see as the pressures and attitudes that affect how well we design good information graphics.

In Mr. Godin’s case, he mentions what he calls “the attention paradox.” While he’s not specifically writing about design, his comments nonetheless aptly relate to the work designers do. As more marketers crave attention, the more they’re willing to part with content that is good at reaching an audience, and terrible at retaining it. Makes sense, right? In a time in which we’re increasingly consumed with tracking metrics and measuring success by the numbers it is par for the course to get caught up in the rat race for the next big thing (big being determined by 30-second relevance and traffic for that day). Surprisingly, information graphics are no exception. And why should they be?

I recently mentioned that, because we’re all under pressure to create more and more content, “repurposing” content is seen as a good way to take advantage of the sweat equity put into other pieces (web articles, reports, data collection) and to convert that into an infographic. This pressure to produce can have real drawbacks–clients mistakenly assume that information can be quickly “designed” just because in their estimation, the facts and the message have already been proscribed. Here–quality can suffer from lack of time. But the point that I was really getting at in my post, which I unfortunately failed to articulate clearly–was the designer’s role.

When designers are treated as service desks and not content experts (“Here are the facts, here is the message, now please make this pretty. Call me when you’re done.”), you simply don’t get the best work.

Fortunately, Alberto Cairo, in “Empower your infographics, visualization, and data teams” gets to the point. According to Mr. Cairo (and I agree) the real problem is the limited perception of the designer’s role. He mentions how, in news rooms, graphic designers are often seen as “service desks.” This isn’t limited to news rooms. In my own life, I occasionally get requests to design graphics “you know, like the New York Times” (yes, I really do). As Mr. Cairo points out, we all laud the New York Times and other large media outlets (one of my personal favorites is New Scientist) for their high-quality information graphics–pieces that can take months to make with large teams of content producers and designers in place. I agree with Mr. Cairo’s perspective that this fact might lead you to erroneously conclude that time and staffing (more people, more time) equals great work (bluntly, he says, “You can’t.”).

The solution lies, in part, in treating and using your designers as partners who help to shape content effectively.

So, what does this mean, exactly? Bring your designer into the room when you’re having editorial discussions about how to create content, before you’ve decided on what shape that content will take. Listen to your designers and expect them to offer up ideas about how to turn that into information design (be it static, motion or interactive).

Designers should read the content.

Expect your designer to read, read, read and understand. I ask my designers to read research reports before they create infographics or data visualizations. This may be a “duh” moment to some of you, but you’d be surprised how many people (including designers) don’t think of this or, worse, don’t see this as part of the designer’s role. How do you design what you don’t understand? How do you filter out the best parts of information and data without having reviewed the source?

And don’t micromanage the design. Leave them alone to create and use their expertise. Trust them, as content partners, to visualize not just the data, not just the facts, but the voice that carries the design.

I’m sure there’s more and would love to hear from you about what other recommendations you have.

Building good infographics part 3: Design and execute

In the first article of this series, we discussed how good planning and team dynamics can make or break even the best design ideas for an info graphic.

In the second article, you learned how to bring together your data and your story into a solid sketch.

In this final part, I’ll cover presenting the concept to your team effectively, managing expectations, and executing the rest of the design process so that your designer doesn’t fire you.

Part 4: Pitch the concept sketch to your team.

Schedule another meeting. Buy more donuts. Bring your designer. Keep it low-key if you can.

Get buy-in and set expectations. Now is the time to present to the team. The nice thing about sketches is that they appear to be so informal that you can share them with people quickly–they allow you to reach out individually to take the temperature of the team if needed and make adjustments as you see fit. This iteration helps because, when you get down to the formal presentation of the sketch, you’ve already established a bit of buy-in (team dynamics permitting, of course).

Before you launch into the sketch, start with a step back to cover the major points from the kick-off meeting.

It may seem repetitive, especially if you just met a few days ago, but it helps ensure that everyone’s on the same page and, if not, identifies those issues quickly. There is nothing worse than launching into a presentation only to find out later that not everyone shares the same goals for the graphic.

Reiterate what you’re creating, who it’s for, how you expect they’ll use it, what they’ll likely want to hear, and how the graphic will support that. Sometimes, this is where things can really get bogged down. The meeting that you had a few days or weeks ago (you remember the one–everyone nodded and seemed to be in happy agreement) suddenly becomes a distant memory as stakeholders, faced with a concrete presentation and decision point, decide to begin reevaluating the goals, objectives and purpose of the graphic. Well, better now than later. When this happens to me, I’m grateful for it, to be frank. It’s pretty much a hallmark of busy teams (you can’t get them to focus until you have something in front of them) or new teams (those who haven’t worked together before, or who haven’t created data viz products before).

Sometimes the difficult conversations tell you more about the team than anything else. At any rate, it’s valuable.

If this happens to you, try to relax and take it in. Sometimes the difficult conversations tell you more about the team than anything else. At any rate, it’s valuable. It’ll give you a sense of the red flags to watch for later in the process. If the team is too indecisive, bring in senior managers, if you can, or summarize the issues they raise and simply state that decisions need to be made before moving further. Don’t be discouraged if you’re suddenly back to the drawing board. These things happen. A lot.

If the conversation is moving smoothly, it’s helpful to talk about the data.

Talk about the things that you expected to find that you confirmed (likely they’ll expect those things also). For example, “I knew that widgets were gaining ground in G20 countries.” And talk about the findings that surprised you. “We talked about widgets gaining ground in our report, but when we looked at more data we saw the lead as slight, which belied our key message. So, to soften this, we added data about projected use for the next 8 years and were able to keep the message of increased widget-use.”

Sell your stakeholders on the problems and solutions that you encountered before getting into the nitty gritty of a sketch.

In other words, ensure that you have shared expectations about the graphic, then sell your stakeholders on the problems and solutions that you encountered before getting into the nitty gritty of a sketch. It’ll inform them and give them good perspective as they review. This sounds like a lot, but it’s important. In my experience, it can take anywhere from a few minutes with an experienced team to more than an hour.

Now, show the sketch(es). Pitch your concepts. Listen. Get people excited. Be open-minded (or, hell, just fake it). Listen, listen, and listen. When you’re not listening, ask questions. Have your designer on hand to participate with you.

Weighing the impact of team recommendations on scope and timing. Once the show and tell is over, talk about the production and design cycles in a general sense and, if you’re able (you’ll have to think on your feet), how the group’s feedback might affect the schedule and scope of the graphic.

Don’t speak for the designer if the designer is not there.

Be careful here–particularly if the designer (for whatever reason) is not part of the conversation. Don’t make assumptions about how easy or difficult it will be to implement a particular suggestion. What looks easy to you may take a long time to illustrate. What seems like a no-brainer idea may not be supported by the data. What seems like a good suggestion may have cascading effects on the design that only the designer can spot. When in doubt, be noncommittal.

Part 5: Keep iterating.

Once you’ve had these conversations, you can keep iterating the sketch as needed. Easy peasy, right? Sure. I usually allow for about 2-3 iterations of a sketch. More than that, and I like to put in a hard stop to bring the team back together to ensure that we’re not going off track. Each iteration should be more refined and have fewer issues. If you find yourself or your team continually revising or revisiting the same things, this may not be a good idea to execute as a graphic.

Part 6: Start illustrating and designing. When do you move from paper and pencil to design?

For me, once the structure, content and data are mostly locked down (this is what we want to say, in this order, this is the data we want to show, and this is how we want to show it) it’s safe to move to design.

  • Before you start designing, confirm that the data is final. Really, really final. Your designer will love you, you’ll save time, and you’ll make fewer mistakes.
  • Once in design, keep your design cycles lean and tight.
  • Remember all the work you put into the initial presentation discussions? Keep referring to the commitments that your team and reviewers made in terms of who sees what, when and how they’re allowed to influence the graphic. (Good luck with that.)
  • Follow best practices. Don’t force your designer to unnecessarily embellish in order to add visual appeal.
  • Again, if you find yourself or the team going through too many edits, stop. Revisit whether this project is feasible.

Understand the nature of edits, who’s making them, and why. Too many edits can be a result of:

  • Too many writers involved (or the wrong people suggesting edits).
  • Team members brought in to give feedback who were not made aware of the original goals, audience and dynamics of the project.
  • People (including the designer or project lead) who lack the necessary skills or experience.
  • Not enough of an overall direction given to team about what the graphic needs to accomplish, for whom and why.
  • Decision-makers not focused appropriately enough to give careful feedback (I notice this a lot when working with people who are very, very busy. You might see a lot of edits and back-and-forth when these folks aren’t able to focus on the product as a whole, and consequently keep “catching” things with each iteration.
  • The data changes.
  • Rushed timing. Assumptions are made about how long an graphic will take to produce by the wrong people.
  • Wrong assumptions made about how “easy” it is to create a graphic from repurposed content.

Part 7: Lessons learned.

No matter how successful or unsuccessful your first efforts, you can learn from them. How you impart those lessons to your team is the subject of another post. But suffice it to say that you should keep a close eye on what worked and what didn’t, and get at least an informal sense from all team members involved in order to refine your process and your team for the next time.

Well, that’s it. Go forth, enjoy, and make things easy to understand for the rest of us. And be nice to your designers.

It’s time to watch (or explain) soccer again

Football fans, it’s Euro Cup 2012 time. If you live in the U.S., like me, prepare to justify your existence to people who prefer to watch other sports. If you live in other countries, I celebrate your freedom to share your joy with anyone within spitting distance. Lucky you.

But surely what unites all of us is the onslaught of football-related infographics, complete with awesome Photoshop cutouts of players, images of the coiffed Ronaldo, exploding piecharts and Pinterest boards too numerous to mention. Thus far, I haven’t found any graphics as funny as the Onion’s World Cup 2010 interactive, but it’s early, so I’m still hopeful–there’s a lot of Photoshop licenses out there.

The Onion's World Cup 2010 interactive

The Onion's World Cup 2010 interactive

If you’re a football fan, I’m sure you’ve already seen this interactive calendar by as well as this fixture schedule, both featured on the CultFootball site.

interactive calendar cult football by

UEFA's Euro Cup 2012 fixtures map

Aside from logistics, there’s the ongoing boycott petition in Germany, sponsored by a GLBT group (and Germany’s first openly gay football player) in protest of the Ukraine president’s refusal to denounce his country’s laws which criminalize homosexuality. The petition asks Angela Merkel to follow France and the UK’s example and boycott the Euro Cup (as foreign dignitaries). We’ll see, she says. This isn’t an infographic, per se, but it’s a visual statement nonetheless.

Euro 2012 anti homophobia

And then there is this, kindly brought to us by the gambling community (reminding us that betting is a huge part of football):


One of my favorite football-related infographics is always the slew of stadium infographics and maps, each one looking suspiciously like high-tech UFOs (why did I say that? Is there any other kind of UFO?). This year is no different. When the little green men/women/whosamawhatsit come to visit, they’ll feel right at home in one of these:

UFO stadiums Euro Cup 2012

And then there’s the Spanish team buzz on social media. Well, it is what it is. Go Spain!!!

Spain and the Euro Cup social media


Should graphics be easy to understand?

Ah, the glamorous life of the data visualization designer… to draw or not to draw? To obfuscate or not to obfuscate? I’ve been doing some reading lately about a debate that is making its way amongst the data viz community. At what point does too much illustration, creativity or innovation get in the way of the primary purpose of data visualization? And how well is the design community being transparent about art based on data versus data visualization? Or, to put it more simply, should data visualization be easy to understand and what happens when it’s not?

Allow me, first, to offer up my own definition, artfully cadged from people much smarter than I and enhanced by my own experience in the field, such as it is. So, data visualization is what, exactly?

Information served up visually in order to inform and improve/enhance our understanding of the data.

Clumsy, but I’m hitting the main points: inform and understanding. If pressed, I would add the word “easily.” Actually, it’s the word “easily” that prompted me to write this.

If you can’t understand a data visualization piece, then it’s pretty useless, isn’t it? Maybe it’s beautiful, but if you walk away more confused than you began, it’s useless. And if you walk away as confused, or a bit less confused, it’s still useless.

How far can we take this concept? Here is a quick survey of what folks have been saying lately. Props to infosthetics for providing a good starting point for these discussions. And here they are:

Stephen Few’s blog post on the two types of data viz is a good start. According to Few (Tufte’s alter-ego), there are two approaches to presenting data graphically—data visualization and data art. As he puts it, “rarely do the twain meet.” Therein lies the problem. They do meet. All the time. Though Few makes a good point—failing to distinguish between them creates confusion and harm, I would argue that the two are not mutually exclusive.

Few defines data visualizations as products created to inform, and “data art” as visualizations of data created to entertain—“art based on data”—something which can be judged accordingly.

My response? Would that the public were quite as discerning as he. The train has left the station and what we have before us is—at worst—a proliferation of eager designers too quick on the draw to consider the very important questions that need to be asked about the data that are being depicted. At best, a cadre of informed (and willing to learn) designers who humbly allow the information, the audience and the goals of the visualization to drive the design—who are loathe to add one extra pixel that doesn’t belong, and willing to take away any element that obscures a better understanding of the data. I’d like to think that I fall into the latter category but I fall somewhere in the middle, as do most designers.

Rather than drawing a bright line between these two approaches and dogmatically refusing to accept a middle ground, I suggest we embrace a blend of these when they are produced well—when they inform and present a clearer understanding of the data and are at the same time aesthetically pleasing. As a designer who chooses to serve both masters—art and data, I find joy in being able to translate a jumble of Excel rows and columns into a plain bar chart—sometimes the beauty lies in the hard work of sifting through the data and simplifying complexity. And sometimes the joy comes from experimenting with different formats and adding visual accents to enhance the data—provided, of course, that the user’s ability to understand the data is not impeded, but enhanced.

Nevertheless, I agree with Few’s depiction of the pitfalls of “data art” being misperceived as data visualization, and I’ll add one myself. In addition to spreading poor practice instead of best practice, it creates unrealistic expectations about what is acceptable in a data visualization, particularly for those of us who are working in the industry in a supportive capacity to researchers and writers with an uneven understanding of best practices (how many of us have been asked to create 3D graphics or exploding pie-charts on a whim?).

And a rising tide floats all boats. In this case, I’ll agree with Few’s point that the proliferation of “data art” and other fancy-schmanzy graphics that pass for data visualization imply that data viz is a closely-guarded secret known only to denizens of the data underworld (paraphrasing liberally from Mr. Few, here). But I take issue with his assertion that this prevents the “democratization of data”—implying that the public is somehow being dissuaded from engaging and creating data. For better or for worse, they aren’t. Just google “infographics.”

As an interesting aside, note that Eagereyes’ Robert Kosara wrote a primer on the two types of data visualization that Few discusses, waaaay back in 2007. Like Few, Kosara was also bothered by the blurred line between data and art. What Few calls “data art” Kosara called “artistic visualization.” Nonetheless, they each underscore the same points—keep data and art separate in order to be as transparent and clear about the data as possible. I agree with the goal.

As Kosara puts it, “looking at one type of visualization expecting the other will lead to disappointment and misunderstandings.”

Kosara, uses what is, in my opinion, one of the best data viz sites out there (infosthetics) as an example of sites that don’t make those distinctions, thus creating confusion. Granted, this was back in 2007. I wonder what he’d say now? Nonetheless, I disagree. Let’s not confuse lack of best practice (for example, normalizing your data to prove a point, and not being transparent about it) with the so-called sin of creating a piece that is visually striking. A designer can produce a graph with no artistic aspirations whatsoever that nonetheless obscures the data. And a designer can produce a terrific visual that observes best practices (to inform) and serves up the data artistically and well.

Adam Crymble has a different moniker for Few’s “data art” and Kosara’s “artistic visualizations.” He calls these graphics “shock and awe.” I love that term. Of all the discussions that I have read, Adam’s make the most sense to me. He doesn’t touch on all data viz that is artistic, but rather focuses on the extreme—and in this I strongly agree with the points he makes.

Adam Crymble: “shock and awe” graphics

We’ve all seen these very beautiful, complex visualizations that belong inside of a picture frame or a screensaver. Or, for a few seconds, they give us pause and food for thought.

a complex visualization of World Economic Forum data

I’ve seen them, written about them and admire them for what they are—unique explorations of the complexity of data. An artistic or visual expression of the complexity of the information we spew out and take in. But they don’t inform in the traditional sense of the definitions of data viz. They may underscore a pattern, convey a sense of weight through sheer numbers or complexity (as the example above does), but that’s about it. They’re pretty much impossible to understand on a granular level without some work.

Adam’s assertion that these complex visualizations have no place in the academic world is beyond my ken. For the record, the example above is mine, not his (see his post for his own, more humorous example). But if he is correct that peer reviewers are afraid to betray their lack of understanding of these graphics, and thus—through tacit acceptance—are endorsing their validity, well then that should concern all of us.

The most interesting point to be gleaned from Adam’s perspective, I think, is the bullying nature of shoving a terabyte of data in front of someone’s face and saying “Aren’t I clever? Don’t you get it?” I don’t. Point well-taken, Adam.

Mark Ravina writes an interesting rebuttal to Adam’s criticism of “shock and awe” graphics. He compares these artistic and complex visualizations to early feminist scholarship that provoked anger when it challenged the systemic sexism of the ivory tower. I’m a huge fan of confrontation and anger-provoking methods to push movements forward. In the early 90s, ACT-UP did the same thing for GLBT rights, if you’ll recall. Without ACT-UP, Queer Nation and Lesbian Avengers, there would be no fancy Human Rights Campaign fundraising dinners today. I get it.

But Ravina’s assertion that these complex visualizations of data somehow push the field forward is a bit much for me. He calls them “intellectual challenges.” I’m not so sure about that. How many of us who are willing to spend more than a few seconds trying to piece together a gazillion threads and data points in a fancy graphic. I think we consider it more of a waste of time to do anything other than admire the concept, the novelty of the presentation and then move on. Intellect doesn’t play a big role here (the creator, on the other hand, gets some bragging rights for creativity). Does it stick? Does it move the field forward? Um, maybe, sometimes?

Ravina spends a fair amount of time discussing how humanities researchers (he knows them better than I, certainly), insist on tables when they ask for data. I didn’t really read that into Adam’s criticism of these graphics—he was merely pointing out that data viz designers were making information too complex—he never claimed that the solution was to create charts. Then Ravina cites the misuse of pie charts to make the point that just because something is familiar, it can be misused. Is he implying that unfamiliar things can’t? As he puts it, “is schlock worse than shock?” Aside from the clever turn of phrase, it’s a bit of a moot point. Nothing that I have read criticizes innovation—merely obfuscation.

Mark Ravina: “Is schlock worse than shock?”

Ravina makes good points. He surveyed (presumably informally) graphs produced in history journals and notes that the bulk of them rely on formats developed (according to him) 200 years ago—pie charts, line charts and bar graphs. And he mentions how slow the field (I’m unclear if he means academics or history journals in particular) has been to adopt and thus understand formats that even today’s eighth graders are learning (box plots, for example). That’s a valid argument, certainly, but it has little to do with the complex visualizations that Adam was addressing or, for that matter, that Kosara and Few discuss. (To be fair, Ravina’s post was mostly in response to Adam’s).

However, he conflates different types of complexity, predictably citing Tufte and Menard (some of you know how I feel about that) as well as Rosling. Perhaps it’s a matter of taste, but I feel that Rosling bends over backwards to make his visualizations inspiring and accessible (not necessarily complex and beautiful), whereas the Menard graphic, while certainly elegant and ground-breaking, does not (of course not, and how could it, given when it was produced).

Lastly, one of the most important concerns that Adam raised was around obscuring data. By introducing unnecessary complexity into a visualization or graphic, data visualization designers can make academic and peer review verification and transparency needlessly difficult. Ravina counters this by saying that liars will lie. I don’t think that’s the point. They will lie, but transparency is as much about spotting errors or raising valid concerns as it is about unmasking willful deceit. Hats off to Ravina for taking the time to provide some very thoughtful counterpoints to the discussion.

Excelcharts is a pretty good resource for charting and data viz in general, despite the name (*smiling*). Jorge Camoes nicely (and literally) draws the elusive line between art/entertainment and data/information. Data Visualization Continuum

More importantly, he puts a restraining hand on eager designers, quite reasonably underscoring Few’s point to make sure that, as designers, we emphasize that charts and graphics are readable and easy to understand, not memorable or beautiful. Of course, I’ll see your readability and raise you ten, Jorge. Let’s make the data understandable and, if we can, beautiful as well.

Lastly, there is this. It is a tome. You could spend hours here. It’s an open-review paper, part of which is around data viz, part of which I have skimmed. It deserves careful reading, and I’m eager to do so and write a follow-up post.

Well, if you’ve hung in there with me, I hope you have learned something. I know I have.

The joys and sorrows of concentric circle graphics

There are not many good examples of concentric circle graphics out there. La Nacion produced one last year about subway strikes, and The Guardian produced an interactive graphic on gay rights in the U.S. Both of these intrigued me because, in my day job, I produce endless variations of graphics dealing with 50-state data. And most of the time, when we look at 50-state data, we draw… you guessed it: maps. Or bar graphs showing quantity or line graphs showing changes and trends over time but no matter what we do, it involves data for the 50 states, most often over time. 50 states multiplied by several years is a lot of lines to draw, bars to fill and state maps to create. So I’ve been thinking about ways to tell the story in different formats–going beyond the map, so to speak. Last Wednesday, we created this concentric circle interactive. Here’s how we did it, and the process we took to decide on the format.

Stateline PCS jobs screenshot

One of the most onerous dimensions to 50-state data is the sheer physical size and length of the data. Our website used to allow for a content well of 500 pixels. Try shoving 50 state labels across 500 pixels and you’ll quickly see why it’s a challenge.

But even with all the real estate in the world, long, horizontal displays are also taxing on the user if there is a comparative aspect to the data. There is simply too much bouncing back and forth from the left to the right. Go long and you lose the comparative advantages of a horizontal layout because users with small screens must scroll vertically and can’t see the entire landscape at once. Of course, layering the data into different views as an interactive can solve that. But sometimes you want to show the data all at once. And for that, a static graphic can work well.

Understandably, a map is often the solution. But maps have their limitations too. There’s only so much that you can infer from a map. If your data consist of more than 4-5 gradations it can be tough to create the at-a-glance, concise overview for which a map is best suited.

And if there are no regional patterns discernible in your map, readers wind up staring at a jumble of color with only a legend to tie it all together.

Which brings me to concentric charts. They’re not pie charts (if you look up pie charts on wikipedia, you will see that there is a distant cousin to the pie chart called a “ring chart,” also known as a multi-level pie or a radial tree). These appear to be somewhat visually similar to concentric circle graphs but have a different use–they tend to show hierarchy in data–you might see these when your computer shows you how much disc space you have, for example.

Filelight disk usage graphic

This ring chart shows computer hard drive disk space

A concentric chart, on the other hand, can tell a different story altogether. In a recent post on La Nacion’s subway strike graphic, I mentioned how designer Florencia Abd manages to plot out a time across four nodes (year, month, day and time) as well as another variable–type of incident/strike. That’s a lot of ground to cover in a static graphic. Imagine doing it in other ways and I’m sure you’ll agree.

La Nacion Conflictos Bajo Tierra

Because a circle is, well, round, its shape lends itself quite well to a relationship-based approach. Not so much a pie-chart (where the user sees the parts in their physical relationship to the whole), but rather using the organic form of a circle to help the user more easily compare complex data. And if you add concentric circles, you take advantage of the hierarchy inherent to those circles to create layers–an intuitive way to order your data–perfect for showing levels or ratings where you use the inner and outer rings to denote the endpoints in a scale (e.g., one thing is stronger, larger or more intense on the outside than it is on the inside) or time, as the subway graphic above shows (the outer ring shows 5 a.m. and the inner ring shows 11 p.m.).

So, what does all this have to do with the U.S. map? As I mentioned, the strength of a map is to show geographic relationships in data. For example, southern states vote “red” (or conservative) in the U.S.; whereas a swath northeastern states might vote “blue” (progressive). For this, a map is helpful because regional differences tell the story and are easy to spot.

But the nice thing about concentric charts is that they, too, can show geography, or any groupings, for that matter. As the Guardian’s example shows, each “slice” of the concentric chart belongs to a state and groups of slices are regions. In the Guardian example, each ring (or level) of the chart denotes a particular right afforded to gay couples.

The Guardian gay rights in the US

My team took this in a different direction. We wanted to show states and regions as well. But we also wanted to show change over time, as well as intensity on a scale. So when the Bureau of Labor Statistics released its employment figures, we had a few choices. We needed to show how changes in employment have affected each state since the recession (from April, 2007 to April, 2012). Because the recession started in December, 2007, we wanted to show how employment looked in each state before the recession, during the recession and how (and which) states were pulling themselves out of the recession.

We could have created an interactive that showed how the same views above changed over time (presumably you’d see a pre-recession view showing states doing well, a recession view showing most states doing poorly, and post-recession years showing mixed results). The most valuable piece of this would be, of course, geographical patterns in the data, if they existed (how did the Rust Belt fare, or the East Coast, for example). You could overlay this with population or any other demographic data to tell an interesting story.

When we looked at the data, we saw that there were not very strong geographic patterns to show. So we decided to create a concentric chart. Why? Because we didn’t have geographic patterns, but we did have temporal patterns (most states did poorly during a particular period of time, which contrasted well with the mixed results that states showed as they were attempting to pull themselves out of the recession, at least in terms of their employment figures). And the fact that we used a circle meant that we didn’t have to create a very long or wide table or chart, and we could stray from the map approach.

We decided to make this a light interactive–by rolling your cursor over each state’s cell you can see a small bar graph showing change in employment over time. This worked for us because our goal wasn’t to show specific numbers (how much employment rose and fell in a particular state), but rather intensity and patterns over time.

The debate continues (check out the comments on Nathan Yau’s post on the Guardian graphic) on whether or not these concentric graphs are merely eye candy when a simple bar or line chart would do just as well. I would opine that, if used correctly, they work well. Let me know if you agree. Here’s a screenshot of our interactive, and you can view the live version here.

Stateline PCS jobs interactive


Bolivia’s global information and communications technology rankings: 2012

I’m beginning to realize that, for developing countries like Bolivia, technology (by that I mean information and communications technologies ranging from cellphones and internet access, usage and affordability to the use of social media) is a chicken-and-egg dynamic. For Bolivia, both the egg and the chicken seem out of reach, though there are signs that some things might be improving.

The World Economic Forum and INSEAD recently released the 2012 Global Information Technology Report which scores 142 world economies on their use of information and communications technologies. Below is an infographic that I designed detailing how poorly and how well (mostly the former) Bolivia is using technology to improve the lives of its citizens and to become modestly globally competitive in, as the report puts it, “a hyper connected world.”

Don’t get too depressed, there are some bright spots. If you’re interested, read more about how a newspaper in Argentina is using open data to circumvent its government’s lack of open data transparency. And if you’re really interested, e-mail me.

The good (rankings out of 12 countries in South America):

  • Bolivia’s political and regulatory environment (as it relates to technology) ranks 7th in South America.
  • Although Bolivia ranks last in business and innovation, it does show a relatively high (3rd) availability of venture capital.
  • Overall, the quality of Bolivia’s math and science education, its educational system overall, and its adult literacy rate all rank 7, 7 and 8, respectively.
  • And, though Bolivia’s individual usage of technologies ranked last (12th), its citizen participation measure ranks a promising 6.
  • Additionally, Bolivia’s capacity for innovation rank (5) is highly encouraging, despite another last place ranking for business usage of information and communications technologies overall.

The bad:

  • One of the most clear challenges for Bolivia is to increase the affordability, availability and reliability of its Information and Communication Technologies (ICT) to its citizens and the businesses that operate within its borders.
  • Bolivia ranks last, or close to last, along almost every index. The country’s overall Network Readiness rank is 12.

Bolivia's Technology Rankings 2012

A Public Service Announcement on rhetoric and logic


David McCandless has published a brilliant infographic on how we/you/I manipulate rhetoric and logical thinking. Whether you appeal to authority, flattery, probability or tradition, this infographic is for you. Faulty deduction or garbled cause and effect? There’s a place for all of us in this chart. As a smart consumer of visual information, I’m sure you’ll appreciate this infographic.

David McCandless Rhetoric and Logical Thinking Infographic