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information graphics

Sub-Prime Charts: Should Data Visualization Be Boring?

by Jorge

In a recent article for the New York Times, Paul Krugman, the 2008 winner of the Nobel Prize in Economics, writes:

“The banking industry that emerged from that collapse [the Great Depression] was tightly regulated, far less colorful than it had been before the Depression, and far less lucrative for those who ran it. Banking became boring, partly because bankers were so conservative about lending (…).Strange to say, this era of boring banking was also an era of spectacular economic progress for most Americans.”

Now that history is repeating itself, I believe that this applies to data visualization too. The 3D pie chart with pseudo-realistic textures, charting tools like Dundas, Crystal Xcelsius and Excel 2007′s charting engine, they all share the same spirit of the times that nurtured the sub-prime lending mess and all that followed. The spirit of the times that rewards illusory short-term results and effectively dismisses consistent, well-founded, long term strategies.

Can’t We Learn?

We may be scared of the future, but are we scared enough? Krugman again:

“Despite everything that has happened, most people in positions of power still associate fancy finance with economic progress. Can they be persuaded otherwise? Will we find the will to pursue serious financial reform? If not, the current crisis won’t be a one-time event; it will be the shape of things to come.”

Many business managers still associate fancy charts with serious decision-supporting tools. This is the right time to change. Eye-candy, “professional looking” charts are sub-prime charts, and if you take them seriously, they’ll do to your business what sub-prime lending is doing to the world economy.

Take a Chart Stress Test

Good charts are invisible. If your audience’s first comments go to your chart format and design, that’s a sure sign that something is wrong. Get back to your charting tool and create a new chart. Do it as many times as necessary. The audience must see and comment the data patterns only, not the chart.

Charts don’t have to be boring. ”If the statistics are boring, then you’ve got the wrong numbers” says Tufte. If you need your daily adrenaline shot, get it from the insights a good chart provides, not from the chart design.

What do you think? Is this crisis creating a serious “back to the basics” spirit that will influence the way organizations optimize their resources, including the time they spend creating useless charts and presentations?

Photo credit: Steve Kay

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Can Edward Tufte Do Business Charts?

by Jorge

1. Tufte, the Father of Eye-Candy Charts

Tufte’s The Visual Display of Quantitative Information, published in 1983, is probably the most influential book in the history of data visualization, and it is likely to remain so for some more time.

In his book, Tufte outlines for the first time a consistent theory of how a chart object should look like and why it should look like that. His guidelines are easy to understand and very quotable, not buried under six feet of abstractions. Think of well-known concepts like “data-ink ratio”, “data-density” or “chartjunk”: they all come from The Visual Display…

However, too often these principles are taken as self-evident, somehow “discovered”, not invented. A fundamental clarification must be made: these are aesthetic principles that Tufte transposes (brilliantly) from Ludwig Mies van der Rohe’s minimalism to the field of data visualization. These are not universal principles backed up by scientific evidence. Some studies find them helpful, some studies say they are irrelevant, but their effectiveness is hard to measure and they should not be taken as indisputable laws (I call this the “what-would-tufte-say syndrome“).

Unlike other authors (Jacques Bertin, Tukey, William Cleveland), Tufte recognizes that only an aesthetic framework can structure the image (color management, the role of non-data objects, how to emphasize/de-emphasize elements in a chart…). This is clearly the realm of graphic design.

Using aesthetics to improve function is probably the major contribution of Edward Tufte to the display of quantitative information. Unfortunately, this idea that a chart can be an aesthetically pleasing object (“Beautiful Evidence”, the title of his latest book, says it all) went astray and gave birth to a whole industry of eye-candy visualization tools.

From Tufte’s positivist point of view, a chart is defined by how well it makes a pattern stand out. It may be boring but, if that is the case, then “you’ve got the wrong numbers”. His faith in human rationality is both charming and frightening…

2. Patterns, patterns, patterns. And something else.

There are so many misconceptions  to discuss about data visualization that we often forget to emphasize this simple true: data visualization is about pattern discovery, finding useful, actionable visual patterns hidden in the data and make them stand out. Let me repeat: it’s all about visual patterns.

Tufte would agree, but here is the fun part: there is nothing wrong with using 3D effects, textures, and all the decoration in the world. Use them! It is your good taste against Tufte’s. You don’t have to like minimalism. Add color, clipart, anything that you think can engage your audience.

I am not kidding. It’s you, not Tufte, who defines your aesthetic program. Almost anything goes. But, whatever you do:

  • Don’t design technically incorrect charts: do not distort a circle, do not use more than one series in a pie chart, do not make an object variate in two dimensions when you are using a single series, etc. Just common sense, really. And, of course, if you want to break the rules, know them first.
  • Don’t hide the patterns: find the patterns and make them visible. Remove everything except the series themselves. Now start embellishing your chart. But remember: every little thing you add multiplies the clutter and makes the patterns harder to see. You’ll have to find that point where the impact of eye-catching decoration on pattern visualization goes beyond an acceptable threshold.

Please note that minimalism was not randomly chosen. Not only it makes pattern discovery much simpler but also creates a framework to evaluate what belongs to the chart and what doesn’t belong. You can reject it, but if you don’t have a different framework you must decide on an ad hoc basis. Unless you are an accomplished graphic designer (and even then), a minimalist approach is a good start and it should help you to find your own style.

3. Emotions, Emotions, Emotions

Let’s face it: you don’t have much choice. If you do not want to sacrifice patterns, the amount of of decoration that you can actually use is very limited.

So, what do you do with that limited amount of decoration? Essentially you’ll try to create the right emotional response. This is not what you would expect from a over-positivist chart that you end up with by choosing the minimalist path.

Refusing to acknowledge the role of emotions in data visualization is a bizarre thing, considering that you can’t remove aesthetics from the equation, and we all have an emotional sense of Beauty. What many hardcore Tufte fans may consider chartjunk can actually keep the audience from turning the page.

4. Edward Tufte and Excel

Throughout his books, Tufte often refers to the higher resolution of paper, and how it outperforms the current screen resolutions. His sparklines are meant to be printed, because only then the fine details can be observed.

In Edward Tufte’s vision, each chart is unique, and deserves the attention of a work of art. He despises PowerPoint and hardly mentions Excel. His charting tool is Adobe Illustrator, where he is in full control of each small detail. He admonishes against patronizing the readers, but he never really discusses the audience as something that should be taken into account when designing a chart.

5. Knowledge Is Built by the User

matrixpermutator

Much as changed in the last 27 years and you may think that Tufte’s The Visual Display… emphasizes the use of paper just because the extraordinary changes in information technology were still in their infancy back in 1983.

Thing is, that’s not the reason. The real reason is that Tufte always thought of a chart as a final product to be printed and handed to the audience, not something that could be manipulated by the audience.

There is a striking difference between Edward Tufte and Jacques Bertin. Bertin’s “reorderable matrix” is dynamic by definition, and and one of my preferred quotes summarizes perfectly his views:

“It is the internal mobility of the image which characterizes modern graphics. A graphic is no longer ‘drawn’ once and for all; it is ‘constructed’ and reconstructed (manipulated) until all the relationships which lie within it have been perceived.”

This was written in 1967, long before the PC was even imagined. Edward Tufte wants to design an efficient but elegant chart, Bertin wants to solve a business problem. There is no contradiction, one is not better than the other. They just serve different masters. (The image above is from Bertin’s Graphic Semiology and shows how a “dynamic chart” looked in 1967…)

Forty years have passed, but a vast majority of data users have no access to dynamic charts, either because they don’t have access to the right charting tools or they are unable to create those charts using their current tools (it is not that easy for a beginner to create a dynamic chart in Excel).

6. The Life Span of a Business Chart

In his essay “The Cognitive Style of PowerPoint” Edward Tufte argues that the tool itself is intrinsically flawed. I agree with him. Tools are not neutral. They can be forced to do things against their will, but that’s never easy. You can create a dynamic chart in Excel, but it is difficult. You can even force Excel to work like Tableau, but that’s like reinventing the wheel. You can create good chart in Crystal Xcelsius, but that’s against its nature.

The point is, you can apply Edward Tufte’s principles by the book, but that means spending hours perfecting a chart in Illustrator and then printing it. I’d love to. Unfortunately, that’s not exactly how things work in a business environment. The life span of a business chart is short and the time to create it, even shorter. We cannot use Illustrator to create business charts.

7. Take-Away Points

Break away from Edward Tufte, but make sure you know why. Add emotion to your charts (rationally). Decide if the level of eye-candy your audience needs goes beyond what you are willing to add. Other things been equal, an interactive chart should need less eye-candy than a static one. Above all, show the patterns (but make sure your audience wants to see them).

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14 Misconceptions About Charts and Graphs

by Jorge

Misconception #1: A Better Chart Starts With… the Chart

Wrong. It starts by asking yourself if you really need one. Perhaps a statistical measure of some sort is good enough, perhaps you should use a table. If your job is to find patterns in a data set and build shared knowledge about it, what really matters is how efficiently the message is sent, and how efficiently it is received by the audience (two different things).

Misconception #2: You Should Master the (Technological) Tools of the Trade

No, you don’t. Just because you know how to create a chart in Excel it doesn’t mean that you know how to create a chart. If you use Microsoft Excel as your charting software then yes, you should learn more Excel (to spend more time with the kids). But you must go beyond technology, or else you end up creating some very stupid charts. Please note that a vast majority of Excel training courses will not teach you what it should (best practices). It will only tell you how to make “cool” graphs, like a 3D exploded pie chart.

Misconception #3: Defaults are good enough

They aren’t. Each chart must be tailored to the specific data set, audience and message. For instance, try to create a graph that clearly displays a large number of series and you’ll fail if you use the defaults (but can do it with clever color coding). And if you use recognizable defaults, like the Excel 2003 charts, you’ll look very, very, lazy (at best).

Misconception #4: Vendors obviously implement the very best templates

(I’ve heard this one recently, and I found it so incredibly naive that I had to write about it.) They don’t. About 90% of the Excel 2003 chart gallery is junk, and you must heavily reformat the remaining 10% to get something useful. Select other tools, like Crystal Xcelsius and the scenario is even worse. And I am unable to create in Cognos something that remotely resembles a chart (people tell me that version 8.4 is a little better).

Misconception #5: Better charts are just “prettier” charts

I hear this all the time. A good chart may look “prettier”, but that’s just an unintended consequence of a design that communicates better. In information visualization, prettiness must be a by-product of function. The very concept of a “better communicator” is sometimes difficult to comprehend, and trying to explain it is a waste of time, because people need to see it in action. You must take the user by the hand and guide him/her. You must force comparisons: “what can you learn about x using this chart?” and “what can you learn about x using that chart?” “how long did it take you to learn x using this and using that?”.

Misconception #6: It’s All About the Wow Factor

It is not. Many marketers and graphic designers fail to understand this. Marketers are hopeless in their relentless search for the wow factor and the eye-catching, “professional-looking” graphs, and graphic designers should know better, but they prefer to sacrifice data on the altar of Beauty (form is everything, data is a nuisance).

The dominant view among visualization experts (namely Tufte and Few) is that “form follows function“: every ornament in a graph should be eliminated, every object must serve a clear purpose, efficiency should be maximized (labeling series instead of using a legend, for instance). Given the extremely low graphic literacy levels among the general population, this may not always be the best approach.

Misconception #7: A good chart displays the actual values

No. If you label each data point you get a useless table over a useless chart. Labels are not only a distraction but often actually hide patterns in the data. Short labels and annotations can, and should, be used to identify or explain outliers or other interesting data points and circumstances. If your audience expects to see the underlying data then add a link to the table.

Misconception #8: Good Charts Should Be Read at a Glance

No, they don’t. The more complex, the longer it takes. It really doesn’t matter if it takes a second or an hour. What matter is how efficiently the graph  communicates. If a chart takes for ever to be read look for bottlenecks: the series are not easily identifiable, patterns are hidden, demands on the working memory are high, etc.

Misconception #9: The More Detail the Better

What we see as detail can be seen by someone else as clutter. Clutter is the natural child of loss aversion and is is very difficult to remove. If you have 12 competitors your audience will want to see the market share for each of them, even if it doesn’t make any sense. Tufte says “to clarify, add detail”, and yes, 12 competitors in a line chart can be made clear and useful, but you must know how to categorize them and provide a framework to help the user (you can use a large number of categories in a pie chart, for instance).

Misconception #10: It’s All About Selling Your Point, No Nuances

In The three laws of great graphs Seth Godin says that “there is no room for nuance [in a presentation]” and your charts should reflect that. Maybe it is just me, but I hate it when I am not allowed to draw my own conclusions because the data made available by the presenter is too biased towards his/her own points of view. Depending on the situation, a clear path that is supported by a lot of details is much better than a yes/no pie chart.

Misconception #11: You Have to Have Color, Lots of Color

Wrong. Color is a very difficult subject. Large surfaces of primary colors like we often see in presentations should be avoided because they are hard on the eyes and, because everything stands out, nothing stand out. A good option is to use grays for non-data elements like grid lines, and pale colors for color-coding. As a rule of thumb, color should always carry some meaning. Use primary colors to highlight a data point or some other small detail.

Misconception #12: A Single Chart is Enough

It is not. We live in an increasingly complex world, and traditional charts are very simple tools. While we wait for a new set of charts to be invented, we can use interaction (see below) and multiple charts to create a richer picture. That’s why scatter plot matrices, small multiples or trellis displays, and specially those multiple variations of executive dashboards are much more powerful than a simple chart.

Misconception #13: Charts Are Interchangeable

They aren’t. You can use a column chart or a line chart to display a time series, but while a line chart performs better than a column chart when reading trends, it is easier to compare data points using a column chart. Most visualization experts will tell you that you should use a bar chart instead of a pie chart (also because it is easier to compare data points), but a pie chart gives you the perception of a whole that is absent in a bar chart. Every graph has its own strengths, and you should select the one that suits your needs.

Misconception #14: Create It and Forget It

Don’t. Making sense of your data is a process of exploration and discovery. A pattern in a subset may be hidden by a noisy background. Different measures may lead to more complex insights. Creating a chart that the user can interact with should always be your primary goal. Unfortunately, that’s beyond the skills of an intermediate Excel user (if you what to learn about interactive charts my Excel dashboards may be a good starting point).

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This post lists 14 widespread misconceptions about charts, but probably is a very incomplete list and you may not agree with all of them. What misconceptions would you add/remove?

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[Update: Jon has been writing extensively about Excel 2003 and Excel 2007 (by the way, it's a great resource that helps us to see through the marketing noise). I said in the comments below that I prefer to use Excel 2007 charts to post images in this blog. He doesn't agree and he tries to prove in his last post that charts in Excel 2003 are actually better. He uses good examples to prove his point but I still believe that this (Excel 2007):

looks better than this (Excel 2003):

Yes, probably there is an "overaggressive anti-aliasing", but the line in Excel 2003 is too "crispy" for my taste. Again, it is just a matter of creating images for a blog, not exactly for serious work...]

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