Posts tagged as:

pie chart

The Wow Factor: How to Use Charts to Impress Your Boss

by Jorge

CarnivalYou are in the middle of a presentation and your worst nightmare suddenly comes true: your boss yawns, and for the right reasons too: your presentation is dull, your charts are dull dull dull and you are boring your audience to tears.

The solution? High impact charts that keep your audience glued to the screen.

What Are High Impact Charts?

High impact, professional-looking charts are designed to impress your audience. Hit ‘em right between the eyes and they’ll keep coming back for more! If you want to create successful high impact charts you should make sure they share some or all of these characteristics:

  • Real life-like objects: people love the sense of “concreteness” you can get from a well-rendered 3D chart;
  • Animation: if you really want to grab the attention of your audience, animation is your safest bet. Use it to add suspense or dramatic effects (you can do this easily using PowerPoint);
  • Hyperlinks: add some hyperlinks to your charts and/or PowerPoint presentations (for example, add a link to a pie chart slice to jump to a detail chart); people love this kind of sophistication!
  • Strong colors: your audience uses bright reads and yellows and greens all the time. They are expecting nothing less;
  • Go to the point: the message should be clear and simple. Don’t add irrelevant details or details that suggest a different answer;
  • Don’t-make-me-think charts: all charts your audience may not be familiar with (like scatter plots) are off limits;
  • Don’t overdo: often people don’t know where to stop: a 3D pie chart with a single slice exploded is fine; if you explode them all, that’s just stupid.

What you Shouldn’t Do

You’ll want to appear sophisticated, you should avoid:

  • Office 2003 Charts: 3D charts in Office 2003 (Excel and PowerPoint) are badly rendered and chart defaults are ugly. Use Office 2007 or try to make your charts using a free online tool;
  • Clipart and background images: While it is perfectly acceptable (and recommended) to use clipart and background images to keep the attention of your audience, please make sure they are a) send the right emotional message and b) reveal your good taste; try to find suitable images in Flickr or Istockphoto;
  • 3D line charts: While 3D bar and pie charts look great, the more abstract nature of line charts make them unsuitable for 3D effects. Use drop shadows instead.
  • Too many charts in a single slide: Stick to one single idea and make your chart big enough to make sure it impacts even the farthest person in the room;
  • Don’t be excessively consistent. Establish a pattern and be consistent, but add some randomness to force people to keep paying attention. A good place to try this is slide transition.

This is not Data Visualization

OK, before regular readers unsubscribe en masse after reading this post, let me add some notes:

  • Solid data management and visualization principles result in an understanding of your data that goes much beyond the simple illustration of some random key indicators;
  • Most managers don’t really care about data visualization because of their own low literacy rate;
  • However, merit is defined by them, based on their biased knowledge;
  • If you want to climb the corporate ladder, what you do must be aligned with their merit criteria, and the way you design and present your data is no exception;
  • The more you know about data visualization the more options you have. Your persona will be defined by what you know and choose to show, not because you don’t know any better.

So, if your next presentation includes an exploded, 3D, flying pie chart, make sure ignorance is not the reason behind it.

There is an unmistakable tension between what data visualization experts preach and corporate practices. How can we find the right balance between a “purity” that takes you nowhere and a practice that makes you cringe? Share your thoughts in the comments…

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Stephen Few, Data Visualization, Eye Candy and the Pie

by Jorge

Stephen Few left a comment in my post “Is Data Visualization Useful? You’ll Have to Prove it“. We all have much to learn with Steve, so instead of leaving the discussion buried in an old post, I thought it would be interesting to make it more visible. Please read the comment then come here and join the discussion. Here is my answer.

Steve, sorry if I sound provocative, that’s not my intention. You are the leading expert in data visualization for business, you are doing a remarkable work with your books, with your blog, with your forum, with your patience to answer posts like mine. I have to be  thankful for that. And I do agree with 95% of what you write. But you don’t want to be surrounded by people who fully agree with you, do you?

The Effectiveness of Data Visualization

You say “the effectiveness of data visualization is well established by a large body of empirical evidence”. I want to believe that too. However in this study Jarvenpaa writes:

“Graphical charts are generally thought to be a superior reporting technique compared to more traditional tabular representations in organizational decision making. The experimental literature, however, demonstrates only partial support for this hypothesis.”

And J.-A. Mayer adds:

“This study refutes the general superiority of visual information in improving the decision quality (‘naive superiority hypothesis’). The choice and design of visual presentation is determined by information structure, decision environment, the decision-maker and the task decision. (…) The successful use of visual information depends substantially on its acceptance by the manager and the environment.”

What do these authors tell us? First, we cannot be 100% sure about the effectiveness of data visualization. Second, there are many other variables at play. And third, managers must accept it. This is a critical factor. Managers love impression management, and making a good impression using the dreaded “professional-looking charts” is the path of least resistance.

Data Visualization Success Stories

I have no doubts that you could share with us many success stories. When I write about an “admission of impotence” I am not questioning your ability to create/lead/mentor successful data visualization projects. But if you want to use those projects to inspire the average person I think you’ll fail most of the time, unfortunately.

Let me tell you how the layman looks like in my part of the world. He makes charts like this:

He believes that a 3D pie chart “looks more precise” and he doesn’t know that Excel chart defaults can be changed (more advanced laymen are able to switch to more “impactful” colors like reds, yellows and bright greens). In my part of the world, a layman doesn’t even know what “data visualization” is about (and they don’t even care). (Here are some more profiles.)

If you are preaching to the choir your conversion rate may be high. But the layman is not easily impressed. You must convert one at a time, and that’s something many of us can’t afford. Can you? He’ll keep making those pie charts because that’s what his manager requires him to do, he doesn’t know better, he’s lazy or you fail to convince him of a causality effect between better charts and better results.

The Layman Must Like Your Charts

In a business environment, charts don’t have to be memorable, only results do. But if you want to change behaviors, your audience must like the new behavior and accept the unavoidable pain. Likable charts help conversion.

You say “I do not discount people’s emotions”. I don’t see it, I’m sorry. The way I see it, you sacrifice everything to the altar of “chart effectiveness”. I don’t find a single one of your charts where the use of color is not purely functional. You say “you should support your claim with concrete examples”. I do have lots of examples: all your charts!

Let me reemphasized this: I agree with you. Chart effectiveness is what we should aim at. But I’m part of the choir. I’m not the layman. I don’t use pie charts.

Pie Charts Again

Unlike most people, I don’t think pie chart addiction is a disease. It is a symptom of a much more serious problem: low numeracy and poor data management skills. Address this problem and pie charts will virtually disappear.

How do you address this problem? “I don’t use pie charts, and I strongly recommend that you abandon them as well.” Researchers like Ian Spence and Stephen Kosslyn don’t think pie charts are as bad as you paint them. Even if they are, it’s very hard to talk people out of an addiction with purely rational arguments.

Perhaps this is my European soul speaking, but I do prefer a gradual approach (“this is acceptable, for the time being”) whereby people (hopefully) start to develop a sensibility to the perceptual issues.

By the way, how come we keep telling people that charts are about trends and patterns, not about the precise figures and then we argue that pie charts are bad because we can’t tell the difference between a 13% slice and a 14% slice? It doesn’t make sense (I’m exaggerating).

We must find more compelling arguments. I don’t like pie charts just because they are a waste of space (low data density) and can only answer very basic questions, better answered using a table. These arguments are good enough for me. I don’t care if we humans are bad at calculating areas and angles. That’s an academic argument that is irrelevant in the real world (I’m being provocative now…).

To Sum Up

You have  a very consistent approach to data visualization and you practice what you preach. You believe that you can convince people using rational arguments.

Mine is a much more comfortable place. I know that eye-candy is a can of worms that shouldn’t be opened. I know that we should protect the layman from himself. I know that simple rules with no exceptions work better than complex rules no one bothers to learn or understand.

But I like the gray areas. I like to protect the poor and the oppressed pies and I try to find their small role in the world of data visualization. The same with eye-candy. The same with emotions. The right amount can get your foot in the door. What is “the right amount”? I don’t know. I’m still searching.

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Charts Are for Sissies (Grumpy Old Man’s Guide to Charts)

by Jorge

Making a chart is so simple that even a chimpanzee can be trained to do it – press F11 and get the banana (that would explain the poor quality of many business charts and presentations – and the raising banana consumption).

To prove that they are better than chimpanzees at making charts, humans invented the eye-candy and its epitome, the glossy 3D pie. Some well-known data visualization experts believe they are poor and useless, nothing more than lipstick (on a pig?).

A don’t agree. A think they are rich and very informative: there is no better chart to tell us that the author hasn’t the slightest idea of what to do with the data. (I am sure there is a strong inverse correlation between 3D pie charts and scatter plots. The more you love one, the more you hate the other.)

This is not just another rant about 3D pie charts. It’s about charts in general, even the good ones. If your only data analysis / communication strategy is to pollute the air with yet another chart then you are fully immersed in the sissy world, and lipstick is all over the place. Charts can help reduce information overload, but chart overload is not better.

A chart is just one of several tools you can use to make sense of your data. You need text, and plain figures, and statistical measures, and tables and yes, some charts. The best results come from the right blend of all those tools.

How do you know if you are a sissy (chart-wise)? Here is a simple clue: if you know how to use and interpret a box-and-whisker plot then you’re on the right track (extra points if you can do it in Excel). If not, do yourself a favor and find a good entry-level statistics manual.

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Is Data Visualization Useful? You’ll Have to Prove It.

by Jorge

Great data visualization is hard to measure: you can’t prove you have a good chart. Unless you can convince your employer to deploy at least two different formats/layouts and are able to compare results, you can say “this is a good chart” but that’s an act of faith, not an act of science.

It’s True Because It Rhymes

Information visualization experts like to evaluate a chart based on its compliance to some more or less accepted standards (Tufte’s data-ink ratio, for example). That’s like saying “it must be true because it rhymes”: the truth is defined by the language itself, not by the real world. Now, please close the curtains of our ivory tower…

I know, it’s not easy to assess the efficiency and effectiveness of good displays. They look natural and obvious, undeserving of praise and, probably, boring and uninspiring. Compare these charts:

Bubble charts

This is a true story: users wanted to evaluate sales territories, one at a time. Color-coding each bubble (Example A) was pointless, while Example B provided context without distractions. Guess what chart they would choose if they were allowed to… (happy ending: they reluctantly accepted Example B). (A word of advice: if you are looking for a promotion, a kindergarten chart variety always outperforms a “serious” chart.)

If your chart is doing a good job at helping people, no one will actually be aware of the chart’s role at making sense of the data. That’s why it is so hard to find good examples of data visualization using standard charts. If people actually like them, they like them because of their usability and/or interactive features.

When Stephen Few asks the readers “true stories about the benefits of data visualization” that’s almost an admission of impotence.  He should have hundreds if not thousands of good examples to share with us, right? Well, I know there are many examples out there, but I can give you none, sorry. Is data visualization some kind of astrology? I know it works. Why? Because I have faith. (On second thought, he is not asking for good data visualization examples. It really doesn’t matter if you use Tableau or Xcelsius, and that’s a relief.)

Opening the Pandora Box

Ultimately, what makes a good chart is how it resonates with your audience. Assuming that your are not unethically distorting the data, a chart that forces people to act is better than another one that only makes people aware of the subject.

If a single chart can save the world, it will not be a Few’s or Tufte’s 100% compliant chart. It will be a glossy Xcelsius pie chart.

(Wow, that’s depressing…)

If you read this blog that’s a clear sign of intelligence and sophistication :) . Unfortunately, you are not representative of the typical data visualization user and/or producer. The real world loves pie charts and doesn’t understand scatter plots.

Here is my Pandora box: give the audience what it expects and understands, even if that hurts your data visualization soul (OK, give it 90% of what it expects and use the remaining 10% to educate it.)

Cultural Relativism? Not So Fast.

Please don’t misrepresent these arguments. I’m not saying that all charts are born equal. There is a reference point and some misconceptions should be avoided A chart that maximizes insights, removes clutter, uses color wisely and clearly shows the patterns hidden in vast amounts of data, that’s probably a good chart and that’s what you should aim for. And yes, you should avoid pie charts.

If you present some sophisticated charts to your unsophisticated audience you’ll lose it. Relax. Draw a line but don’t forget the candies. You can take a horse to the water, but you can’t make him drink, unless you give him some sugar cubes…

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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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How Many Computer Scientists Does It Take to Screw Up a Pie Chart?

by Jorge

You can add  silly 3D effects to a pie chart, you can explode all the slices, you can compare multiple pie charts, you can use a legend instead of labeling the slices directly. This will probably render your graph useless, and make you look kind of dumb, but it is not the end of the world-as-we-know-it. But when making a pie chart there is something that you should never ever do, a capital sin that will make you burn in the hell of information visualization: using more than one variable in a single graph.

Well, since we are witnessing the end of the world-as-we-know-it, computer scientists at the University of Utah decided to give a little push, visualization-wise. They are designing a computer application “they hope eventually will allow news reporters and citizens to easily, interactively and visually [analyze] election results, political opinion polls or other surveys”. They boldly state that they “have developed new techniques for exposing complex relationships that are not obvious by usual methods of statistical analysis” (press release). And what are those new techniques? A doughnut chart:

The outer ring labels the series and the inner ring displays the data. Apparently you may add as many series as you wish and you can filter the results by socio-demographic characteristics. There is a video demonstration here [via].

This is the kind of joke that I would expect to be related to April Fool’s Day, but they seem to be serious about it. No one told them that showing part-of-a-whole is one of the few strenghts of circular charts, that when people see 52,7% they see a pie cut in half, not a quarter, that “whole” mean 100%, not 200% or 300%.

Regular readers know that I rarely utter such harsh comments on visualization ideas and applications (I even tried to create a dashboard using Crystal Xcelsius), but this is the stupidest idea of the year. They should know better (here are some tips).

By the way, I found this through a post by Sarah Perez at ReadWriteWeb. She writes: “unfortunately, the poll-analysis software isn’t quite ready for prime time. What a tease!” Fortunately, it is not! And judging from other posts, they could use an information visualization consultant. 

Well, perhaps I’m missing something. Am I?

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How to Make Better Pie Charts with On-Demand Details

by Jorge

This article goes much against conventional wisdom about pie charts (and doughnut charts) by answering these two simple questions:

  • Can we use a large number of categories in pie charts? (Yes, we can.)
  • Can we make a productive use of the apparently useless doughnut chart? (Yes, we can.)

Disclaimer (Sort of…)

Let me start by declaring this: I believe that the analysis of simple proportions is, by its very nature, very limited. It only scratches the surface of the data and it is useless for serious, decision-making processes.

A circular chart is poor because the underlying message is poor. If you can run a business using pie graphs to make sense of your data please let me know what market are you in, because I want to be there too (well, not really…).

Pie chart belong to the media and to some simple presentations. Leave them there. And don’t make the charts you see in the media your role model.

The part-of-whole issue

That said, one must recognize that proportions are so pervasive and hard-wired into our brain that escaping them is almost impossible.

A circular chart conveys perfectly the idea of part-of-whole relationship. You can’t use a bar chart to show this relationship because the whole just isn’t there! Yes, you can use percentage scales, yes you can say it in the title, but it isn’t the same thing, is it?

As I wrote in my previous post on loss aversion, each chart answers a question from a different perspective. Charts are not interchangeable.

Often pie charts are used just because they may look better (this is, of course, in the eyes of the beholder) but what the user really wants/needs to know would be better answered by a bar chart. This is a problem of graphic literacy and information management. It has nothing to do with the intrinsic qualities of pie charts.

The limit of 4 to 6 categories in pie charts

There is a widespread believe that you should not use more than four to six categories in a pie chart.

That’s is wrong or, at the very least, very incomplete.

In fact, you can use as many categories as you want, and still get meaningful insights from the chart. Problem is, you must know what to do with your data (graphic literacy and information management, again), and a large number of bad charts come from this simple fact: people don’t know what to do. Garbage in, garbage out.

“The Secret Strenght of Pies”

Here comes the fun part. In an article published back in 1991 by Ian Spence and Stephan Lewandowsky, titled “Displaying Proportions and Percentages” the authors write:

“the pie chart outperforms the bar chart for complicated comparisons, suggesting that the perceptual addition and comparison of components is inherently easier with the pie chart than the bar chart.” (emphasis added)

(By the way, the authors also say that this advantage will be lost if you “explode” the slices.)

Stephen Few, in his “Save the Pies for Dessert“, cites this article and writes about “the secret strength of pies”:

It is not difficult to believe that it is somewhat easier to sum the areas of slices in a pie than it is to imagine the combined heights of bars stacked on one another.(…) Regardless, the fact remains that a comparison of two sets of summed parts is rare in the real world. But, by all means, should you ever need to display data for this purpose, a pie chart would serve you well.

Please note that Stephen Few, in his highly regarded book “Show me the Numbers” says:

“I don’t use pie charts, and I strongly recommend that you abandon them as well.”

Few acknowledges that pie charts “could serve you well” in a very limited set of circumstances (“a comparison of two sets of summed parts is rare in the real world”).

Is it really rare? It may be, but that’s because people don’t know what to do with their data (again). Let’s see.

You have 10 or even 20 categories and you want to use them all (your loss aversion tendency?). Because 20 ungroupable categories are rare in the real world, you should be able to visually group them, using a color (hue) for each group and a different saturation for each category. By doing this, you are adding layers of detail, and the reader will be able to select the level of detail that suits his/her needs. This works best when using an interactive chart because you don’t have to label everything (just use your mouse to identify on-demand the more relevant detail categories) but even a static chart can be used (in this case, label only the relevant details).

The Consumer Expenditure Chart

I used this methodology to design the consumer expenditure chart above, with living expenditure (on the right) and discretionary expenditure (on the left).  As you can see, living expenditure accounts for almost 60% of the total. That’s something you can’t easily see with a bar chart.

Then, there is a second level of detail, where you have categories like Housing (more than half of living expenditure) or Transportation. And finally, you could use your mouse to identify those detailed categories in the outer gray ring.

I’ve added some arcs to compare the profile of total consumer units to consumer units with five or more persons. Each arc always starts at the same degree of the corresponding slice. Different proportions lead to gaps or overlaps. Please note that this is not a core feature of this chart. Just wanted to play a little with comparisons (an obvious issue: since the first arcs are closer to the center, a gap between them is different than a gap between the last arcs).

The Secret Strength of Doughnut Charts

As we saw above, pie charts are better than bar charts when comparing proportions. But, as soon as you add a second pie chart you are trying to compare proportion A1 with proportion A2, not proportions A and B of the same pie. There is a shift in the analysis and the pies become useless (use bar charts instead).

Just because you can merge both pie charts in a single doughnut chart it doesn’t mean that you gain efficiency, because the essential problems remain in place.

For many, a doughnut chart is a bad mutation of a bad chart. But if, just if, two bad’s become on good? Could a doughnut, if correctly use, become a kind of pie chart on steroids?

Let me emphasize this: never use a doughnut chart to compare series. I don’t, and I strongly recommend that you should avoid it as well… Always use a doughnut chart to add detail to a series. That’s the secret strength of doughnut charts.

And please, please, could someone write an article on doughnut charts for the English Wikipedia?

I made this chart in Excel

In case you are wondering, you can make the Consumer Expenditure chart in Excel, 2003 or 2007. Instead of the default theme colors, I used some of the colors that will be available in Chart Tamer (thanks, Andreas!).

Conclusion

Pie charts do not deserve their bad reputation. They seem to be more efficient than bar charts in some very specific tasks, like  comparing combined proportions. We should take advantage of that by adding multiple levels of detail. We shouldn’t be afraid of using a large number of categories, provided that those levels of detail are clear and meaningful.

The doughnut chart is the most misunderstood of our chart toolbox. It is seen as completely useless because two series should not be compared using circular charts, but that’s not what doughnut charts should be used for. They should be used to extend the power of pie charts, managing efficiently the level of detail that we need to add to create more insightful charts.

Is this a good way to use pie and doughnut charts? Please share your thought in the comments.

[Update: If you want to know how to create this chart (with a bonus hole-remover...) Jon has a detailed explanation here.]

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Pie Charts – A Devil's Advocate Point of View

by Jorge

In what seems to be a post-vacation syndrome, I am in the mood for pie charts. I see them everywhere, even in car logos.

Actually, I am more in the mood to defy current “crowd wisdom” about pie chats.

Search the web for “pie chart” and you’ll get more than one million results, and a depressing picture of human knowledge. Browse the first 100 and what do you get? Some educational(?) sites (poor kids), tutorials (Excel, php, java, Illustrator), humor (here, here, here), bad (here, here, here, here, here or here) or just plain stupid examples. You’ll also find them in in court or fighting government (who could ever imagine that?). I’ll leave for another post what the Wikipedia and the pie chart thread in Tufte’s Ask E.T. say about pie charts (Stephen Few’s Save the Pies for Dessert is not listed within the first 100 results).

An old litany

Some of these sites discuss the use of pie graphs, but they usually recite the same old litany: our perception is bad at judging angles, you should use no more than five or six categories, don’t use them to compare series, Cleveland’s findings, etc. (there also is at least one unfair comparison between pie and bar graphs and one very aggressive rant against them).

If there is something that I would like to have written about pie graphs it is this Expert notes at ManyEyes:

Pie charts have a mixed reputation. They are popular in business and the media but many information designers have criticized the technique. Some claim that the pie slice shape communicates numbers less exactly than other possibilities such as line length. But this remains unclear in the context of proportions: for example, we have seen no studies that looked at the task of judging whether an item is more or less than 50%. It’s also unclear whether exact communication of numeric values is the only evaluation criterion; at least one study indicates that use of a pie chart for analyzing a problem as opposed to a bar chart changes the way people think about the problem.

This is clearly more constructive than saying that “they are as professional as a pair of assless chaps” (less funny though).

Not all charts are born equal

Current wisdom presumes that bar graphs and pie graphs are equivalent. For that reason, bar graphs should be used, always. After all, they are more efficient, right? But if they are not equivalent, as the above quote suggests? Take a time series, for example. If you want to see trends, you’ll choose a line graph; if you want to compare data points you’ll use a column graph. They are very similar, but by choosing one or the other, the designer is making a choice of how he/she’ll  look at the problem. Bar graphs and pie graphs are very different, so shouldn’t we think twice before selecting a bar graph because of its presumed superior efficiency?

This disdain for pie charts has its roots in Cleveland’s work and in Tufte’s and Few’s writings. Their positivist view towards information visualization may be as relevant as the classic economic theory and its presumption that consumer always take the rational decision, but are we not all predictably irrational? I agree with Robert at EagerEyes when he says:

There is no doubt that we need to be careful about the choice of visual representation, and that we need to encourage the use of good charts and criticize the bad ones. But that doesn’t mean we can get lazy and squeeze everything into a few standard charts types we’ve been using for decades. That is especially true if we want people to actually care about what we’re trying to show – and not bore them to tears.

We should probably try to be more rational and circumspect in a decision-making environment and do not use the media as our role model, otherwise business visualization may become useless. However, ruling pie charts out is not the wisest decision.

Simple rules are made for beginners. Let’s break some. How about this one:  “you should use no more than five or six categories in a pie chart”. Are you sure?

(Before that, we must re-read what Cleveland said and what others said about Cleveland. That’s the next post.)

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