But what Nathan's version is missing is the story. The additional data mostly adds confusion: move your mouse over the year in the lower right, and what do you see? Lots of points are moving around, but there doesn’t appear to be a clear trend. The additional categories are interesting, but what do they add?

Not much. When I was putting together the graphic, I was hoping for a clear trend — something so obvious that didn't have to be explained. Instead I got fuzzy results. And that's where I stopped. On the other hand, the NYT version explains those fuzzy results, namely the outliers, such as women CEOs who work for non-profits or the greater percentage of men in medical specialties like surgery.

In analysis, assuming the users are experts of their data, annotation is less important. It's about allowing them to stay nimble and ask/answer a lot of questions. Graphics that tell stories with data, however, already have something interesting to say.

When the designer places two series on the same chart, he or she is implicitly saying: there is an interesting relationship between these two data sets.

But this is not always the case. Two data sets may have little to do with each other. This is especially true if each data set shows high variability over time as in here.

This seems to happen a lot when people take the data-to-ink ratio too literally or they're trying too hard to be clever within a given space. Overlays work on occasion, but I can't think of any that did off the top of my head. Most of the time it's better to split up the layers into multiple charts.

Infographics can be great as part of presentations, newsletters or other research content. It keeps people's interest by lending a storytelling and visual element to what can be sterile research.

The second said:

Infographics are outstanding for bringing life to content that would otherwise be dry, uninteresting or unshareable.

And the last one, who to be fair, seems to know more than the first two, said:

At the end of the day, the main use for infographics is to create content that can potentially go viral and drive traffic, links and exposure to a Web site and the brand.

If I were new to these infographic things, my main takeaway here would be that they're used to make boring material interesting. Shouldn't it be the other way around though? Information graphics are interesting because their foundations of data and um, information are worth looking at in the first place. Don't fall into the trap of trying to make something "visually compelling" without anything to compel with.

Related

• The path to successful infographics
• On low-quality infographics
• A Little Bit of Design Goes a Long Way With Infographics

So do the renowned folks at the NY Times Graphics Dept. prefer fast or slow thinking visualisations? I asked them what they think makes a successful map. Archie Tse said what I hoped he would: the best maps readable, or interpretable, at a number of levels. They grab interest from across the room and offer the headlines before drawing the viewer ever closer to reveal intricate detail. I think of these as rare visualisations for fast and slow thinking. The impact of such excellent maps is manifest by the popularity of atlases and why they inspire so many to become cartographers and/or travel the world.

A graphic that takes a little while to understand doesn't always mean it was a failure in design. It might mean that the underlying data is hard to understand. Likewise, a graphic that isn't what you expect might let you answer different questions than from the usual standby.

[Spatial Analysis]

After a chat with his color deficient friends about how Vincent van Gogh's paintings seem to appeal to all eyes, Kazunori Asada used visual filters to see how the paintings looked to the colorblind. The experiment produced some interesting results and musings:

Was van Gogh partially color vision deficiency (anomalous trichromat)? Perhaps using a strong color vision deficiency (dichromat) simulation was the wrong approach. How about carrying out the simulation by removing the middle portion of normal color vision, maybe then I could see van Gogh’s pictures in a better light?

The color choices for van Gogh's popular paintings seem less out there with the filters. The greens in the sky of Starry Night, for example turn to yellows.

A colorblind van Gogh though? Probably not. Either way, don't forget to pick your colors wisely. Asada has an easy-to-use tool to see what your own images look like to others.

[Asada's memorandum]

Amanda Cox, the chart marker, on how her work and goals have changed over the years:

At one point — I call it my impressionist phase — I was really interested in making things abstract but interesting and beautiful. And then I had a "curves are fun" phase for a while where I was really into curved things. And then I had an "intentional simplicity" phase for a while, like, how stripped down can you make something and have it still be interesting? I don’t know what my current phase is, but it's kind of an "aspirational reporting" phase. I'm not that great of a reporter yet, but I'm thinking a lot about how we can stop using the same information that's already on the Internet and just remix that. I want to start working with more, deeper information, information that's harder to surface.

This is coming from someone who has won an international award for being the best. So much to learn, I have.

[Substratum]

Related

• Smart people talk about art and data
• What Do People Want to Do With Their Lives?
• Quick time series visualization with Cube

Obvious statement: there are infographics that are horribly made. Some are way too big for the information conveyed and others are useless because the creator had no idea what he was doing. Some infographics are both. Here's the thing though. There's plenty of suck of everything online, and yet somehow we manage to find the good resources, applications, and sources of endless entertainment.

A couple of years ago, infographics spiked and even what seems like subpar work now, passed as amusing at the least. It's like the time on the Web when it was pure awesome to have a site decked out with animated GIFs, blinking backgrounds, and delightful MIDIs that were a treat for the ears. Sites like this still exist — some just as an archive of the past and others by someone learning HTML with a book they checked out from the library — but you'd never mistake one of those sites as an example of great Web or interaction design.

Or how about blogs? It's as easy as ever to start one and you can write whatever you want. Some are bad and others are good. That's how it works when people make things.

Think of infographics/visualization/etc as a medium instead of a method and it should be much easier to unruffle your feathers. There are textbooks and there are novels. There are documentaries and there are slapstick comedies.

So I don't think a flood of low-quality infographics is going to obscure the work that is actually worth looking at. If anything, it's going to be easier to find the standouts.

I've seen this through my inbox already. Readers used to send me a lot of work that well, wasn't the best, but they thought it was interesting. That doesn't happen so much anymore though, because I think we've learned what was made as commercial linkbait and what deserves our attention. Sometimes they are one and the same, which is fine by me. Even if a piece looks beginner, it's easy to tell when a person is interested in the data or is eager to learn how to make things. I'm all for that and am more than happy to offer my two cents when someone asks me. After all, I'm still learning, too and I'll be producing plenty of schtuff in the process.

People can (or will learn to) spot the B.S., and the bad stuff doesn't get shared and doesn't spread.

When the low-quality infographics stop is when we should be most concerned. It means people have lost interest. That's not going to happen any time soon though.

When we say on the phone "I'll be there in half an hour" it’s quite likely we'll arrive sometime in the next 25 to 35 minutes. But for the context of meeting up with a friend "half an hour" will do. If you said "see you in 27 minutes" that would raise a laugh being an odd level of precision for the given context. The same ideas apply to numbers in journalism.

Important in both accurate representation of data and readability.

Written by Amy Balliett of Killer Infographics, the post in question is basically tips for how to create linkbait that doesn't work. Or at least I hope it doesn't.

Let's take it from the top.

Infographics are visual representations of information, or "data viz" as the cool kids call it these days. The term "data viz" comes from "data visualization," which implies that sets of data will be displayed in a unique way that can be seen, rather than read.

I'm not going to get into the semantics. That's for people who are smarter (and cooler) than me, but the obsession with a visually unique result is overdone. Sure, you want your graphic to be compelling, but that comes from the data or the information. You're just trying to make crap look like cake if you do it any other way: it looks good from far away, but as soon as you take a taste, you realize you have poop in your mouth.

Next up, which refers to the graphic above:

What’s wrong with this infographic? It breaks the first rule right out of the gate. When you have an opportunity to display information visually, take it. Here, the tweets per second could have at least been shown in a bar graph.

What? We all know the first rule of infographic design is to not talk about infographic design. I guess that's implied. After that though, the first thing wrong is that there are numbers? What about that gauge on the left without any labels? What does it mean?

Most run-of-the-mill infographics take a few data points and make it look like a lot with big things. This is both a reflection of the data on hand and the creator's data comprehension.

It gets worse.

Two alternatives to the above are offered. Do you choose the utterly boring, horribly regular graph A or the awesomely, cool, and round graph B?

Balliett's suggestion was to use the speedometer, basically because it looks awesomer. Something else comes to mind.

I hope you didn't answer graph A. If so, you're wrong.

If you answered Graph B, you’re catching on. Of course, not all data lends itself to creative and unique graphs. Graph A might work very well if the rest of the infographic shared a similar aesthetic. Sometimes you just have to bite the bullet and produce a traditional bar graph or pie chart; nevertheless, always consider ways to dress it up [...]

It's that obsession with unique again. The problem isn't the bar chart. It's that there's only five data points occupying a lot of screen space. Plus, the speedometer makes tweet rate look way more than it really is, because the method uses arc length as its visual cue.

A more useful graphic would provide more context to the events that these numbers describe. What made the Women's World Cup Final so much more exciting than Super Bowl 2011? Which final was it? Was it really more exciting? When were these measurements taken? Are they the peaks of each event or is it a mix? Were there more tweets because there were more Twitter users at the time? Was it because the time of the day each thing happened? Tell people something — anything — more about the data.

The second half of the post covers mostly general design tips such as color and focus, so it's less rough, but the examples aren't any better.

Bottom line: when it comes to information graphics, it should always be data and information first, and then you design around that. For that to happen, you have to learn more about data — how to work with it, where it comes from, and what it represents in the real world.

Now how about some real dos of data and information design? Leave a DO in the comments below by the end of this Friday, and a signed copy of Visualize This goes to a randomly picked doer.

Related

• Graph Design Rule #1: Check the data
• Graph Design Rule #2: Explain your encodings
• Non-profit Seeks Infographic Designer

During all the meetings, there were recurring themes about what visualization is and what it is used for. Some people really got it, but others were new to the subject, and we ran into a few misconceptions that I think are worth repeating.

Here we go, in no particular order.

Visualization is for making data flashy

This is probably the most common one. It's easy to look at a lot of the best visualization projects and want your data to look and feel the same way. So people ask, "I have such and such data. Is there a visualization technique that I can use to make it look cooler?"

Well, maybe. Not if you only have five data points though. You can spend a lot of time with icons or fancy print, but the graphics are interesting because the data that the visuals represent is interesting.

For example, I mapped the growth of Walmart a while back (It's amazing how much mileage I get out of this graphic.), and people seem to like it because of the organic growth pattern. It starts in one area and spreads outwards like a virus.

Okay, compared to Toby Segaran's original, I did add some interactive flourishes, but even without, the growth pattern is what makes the animation interesting.

For example, here's a map with the same style as my Walmart one, but it shows the spread of Target. It's not nearly as fun to watch, because Target took a more opportunistic approach of expansion. Locations pop up kind of randomly at times. It's mostly interesting as a contrast to the Walmart map.

It should always be data first. Certain graphics get eyeballs because they show something that wouldn't be seen in a table.

Software does everything

There are a lot of options for visualization, and the "best" one will change depending on who you ask.

Personally, I use a lot of R and have a lot of fun in Illustrator. More recently, I've been working with JavaScript. Flexibility is a huge plus for me, and I like to have full control over how my graphics look and how the interactive ones work. Most of what I do though is to present data to a wider audience. If I were an analyst tasked with digging through a large dataset, I might take a different route before I make something custom.

My main point is that there is no one piece of software that will do everything for you.

Some software is good for analysis, some is good for specific types of analysis, and some is good for storytelling.

The more information in a single graphic, the better

A misstep a lot of people take when they're trying to advance "beyond Excel" is to layer too much information on top of their basic graphic. I'm all for providing context and highlighting interesting spots in your data, but at some point it's better to split your one chart into two or three charts.

Some people try to be clever by using multiple axes on a single plot or multiple visual cues in a single chart to save space. Again, this works sometimes. A lot of the time it doesn't. Oftentimes, simple and clear is better than clever and compact.

My favorite test is to show a graphic to someone who doesn't know the data and isn't a visualization expert and see what they take away from the visual.

Visualization is too biased to be useful

There's a certain amount of subjectivity that goes into any visualization as you choose what data to show and how to show it. By focusing on one part of the data, you might inadvertently obscure another. However, if you're careful, get to know the data that you're dealing with, and stay true to what's there, then it should be easier to overcome bias.

After all, statistics is somewhat subjective, too. You choose what you analyze, what methods to use, and pick what to point out in reports.

News organizations, for example, have to do this all the time. They get a dataset, decide what story they want to tell (or find what story the data has to tell). Browse through graphics by The New York Times, and you can see how you can add a layer of information that objectively describes what the data is about.

It has to be exact

If you're using visualization to show the exact value of every single data point, along with every standard error, you're probably using it wrong. Accuracy is important. Yes. But visualization is less about the individual values and more about the distribution of them over time and space. You're looking for (or showing) patterns. You're comparing and contrasting.

If all you care about are individual data points, you might as well put it in a table.

Are there other common misconceptions that you can think of?

Related

• Why Isn’t Data Visualization More Popular?
• Creating Effective Visualization
• Open thread: What’s the difference between a visualization and an infographic?