Posts tagged as:

data visualization

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…

{ 12 comments }

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.

{ 9 comments }

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.

{ 3 comments }

Spiffy Charts

by Jorge

Regular readers know that English is not my mother language , but are kind enough to forgive me for my many mistakes.

I am always willing to learn. Today, while researching for an upcoming post, I came across an expression  I never heard before: “spiffy charts”. I felt in love with “spiffy charts” the moment I read it. And I read it straight from the horse’s mouth (I mean Microsoft).

If you don’t know how to make a chart and are keen to preserve that blissful ignorance, I highly recommend Microsoft Office Online Training, specially the module Create a professional looking chart (regular readers also know how I love “professional-looking charts“). You’ll learn how to “customize your charts to make them more attractive, memorable, and effective“. This means useless charts.

So, let’s see how to turn a humdrum (this is a new word, too…) chart into a spiffy one. First, to declutter your chart remove grid lines:

Ugly Excel bar charts

As you know, grid lines are useless, specially if you don’t care about the data. I would remove the gray background and the border around the legend. And I’d give the chart a more descriptive title to tell the users what they are seeing, but that’s my personal taste.

Then you should remove the y-axis and add labels to each column, further “decluttering” the chart. At this point the readers start sighing for a nicely designed chart table.

Ugly Excel bar charts

Want to give your chart a little more “flair” and make them more “professional-looking”? Just add a gradient fill and a subtle shadow:

Ugly Excel bar charts

Now comes the spiffy part. Imagine that you have a 3D column chart with two series, and one obscures the other. What do you do? No, you can’t remove the stupid 3D effect (remember: you want to make t spiffy chart, not a humdrum one). Well, all you have to do is to change the order of the series:

Ugly Excel 3D bar charts

Much better now, don’t you think? They accept that 3D charts “can be more attractive, but sometimes more difficult to read accurately” (surprise, surprise!). Apparently that’s a detail in the grand scheme of things. You are excused from making accurate charts if you are making professional-looking ones.

So, what else can you do to improve your chart? Ah, yes: the y-axis in a humdrum column chart always starts at zero. We don’t want that, do we?

Ugly Excel bar charts

Now you know how to make inaccurate, professional-looking, spiffy-with-a-flair marmalade charts. Please go straight to the kitchen, make some real marmalade and forget all you’ve learned about data visualization in the Microsoft Office Online Training.

(This is not a real Microsoft Office Training site, is it? It must be some kind of spoof site, and I fell for that trick. Right? Right?)

Sigh…

{ 10 comments }

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…

{ 11 comments }

Animation, Interaction and Dynamic Excel Charts

by Jorge

If you want to add animation to your charts that’s a clear sign that you have too much free time. Go out and play with the kids instead. :)

Yes, animation is a powerful attention-grabber, even more powerful than a glossy 3D pie chart in Crystal Xcelsius. And yes, it can actually be helpful (from time to time). But.

A good example of animation in data visualization is the famous Hans Rosling’s TED presentation, where a long-term pattern is clearly seen (at min 4:00):

This works well because the trend is easily identifiable and you don’t care much about the details. It would be much more difficult to make sense of this data if there were multiple trends and short-term variability. (More on the use of animated charts.)

After watching this presentation people often ask me: “Wow! Can we do that in Excel?” Wrong question. The right question would be “Wow! Can our data do that?”.

You can always create a simple animation in Excel, but it’s hard for a non-programmer to get a smooth transition effect. Here is an example from my dynamic Excel dashboard:

Although you can see a pattern emerging, you would need to add a complex interpolation routine to make it look better (read Jon’s post to see how a simple interpolation can be used).

Animation is better used if there is a pattern to be discovered, but you need something more: the ability to interact with the data. You must be able to stop, go back, get the details. While you can create a simple animation effect with a for/next loop in VBA, interaction with the chart is much easier using a Google spreadsheet with a motion chart:

(This is just an image. Click here to play with the chart.)

Dynamic Charts in Excel

In Excel, instead of creating a VBA routine, consider using a scroll bar linked to the value you want to change (year, for example):

Using a scroll bar adds some level of interaction because you can scroll back and forth, pause and examine the details for a specific year. Obviously, you need to change the chart data source dynamically.

Let’s recap how to create a dynamic chart in Excel. You can do it by changing values or by changing the data source itself (using a different range).

Option #1: Copying the Data

The range A1:E11 is our data set and we are comparing regions. Moving the scroll bar at the bottom changes the year. Column F is our data source. You can enter this formula there:

=OFFSET($A$1,ROW()-1,MATCH($A$13,$B$1:$E$1,0),1,1)

ROW() gets the current row and MATCH() returns the position of value 2002 (A13) in the range B1:E1. So, the formula in cell F2 reads something like this: start at A1, go down one row, go right three columns and get the data in that cell (range of width 1 and height 1). When the user selects a different year the data is copied to column F.

Option #2: Using a Dynamic Named Range

The second option is to create a dynamic named range. Create a named range and, instead of entering a fixed range, enter this formula:

=OFFSET(Sheet1!$A$1,1,MATCH(Sheet1!$A$13,Sheet1!$B$1:$E$1,0),10,1)

As you can see, it is very similar to the one above, but now the number of rows down is fixed (1) and it returns a range of 10×1 cells (because we have 10 regions, but if the number of regions varies you may use COUNTA(A:A)-1 to count the number of regions, excluding header – and don’t forget to move the year to a different cell…).

When you verify your range this is what you get:

You just need to use this range in your chart (when entering the range you must add the workbook name: “=Book1!SourceData”). You’ll also need a range for the category axis labels:

=OFFSET(Sheet1!$A$1,1,0,10,1)

You can download the spreadsheet here.

Take Aways

If you want to animate your charts make sure you do it because it adds value, not because you want to show off your skills. You can do it in Excel but it looks much better in Rosling’s Trendalyzer or in the Google spreadsheet gadget.

To create animations your data source must change dynamically, and that requires some work (and skills). I advise you to shift your focus from animation to interaction and use all that work to design a better user experience. Do you prefer a “wow!” (animation) or a “wow, thank you!” (interaction that actually helps the user)?

{ 3 comments }

Seth Godin Is Getting It…

by Jorge

From time to time, Seth Godin comes to visit our little field of information visualization, and I’m pleased to note that he is learning…

Today’s post, “How to make graphs that work” is remarkably better than “The three laws of great graphs” or “How to make a PowerPoint chart”. Today he warns  us against Excel and PowerPoint defaults and templates, invites us to tell a story, by following some simple rules and braking some other rules. It’s clearly a post on the safer side…

But I do have some remarks. Godin says:

when you show me something exactly like something I’ve seen a hundred times before, what do you expect me to do? Here’s a hint: Zzzzzz.

Right. That’s the problem with defaults and templates. But it’s a problem with all defaults and templates, no matter how good they are. So, what can you do? First choice: be relentlessly creative. Can you do it? Good for you, I can’t. Second choice: make your charts invisible. Show the message, not the chart.

Pie charts are spectacularly overrated. If you want to show me that four out of five dentists prefer Trident and that we need to target the fifth one, show me a picture of 5 dentists, but make one of them stand out. I’ll remember that.

I actually prefer the Presentation Zen style. If you have two slices, you just need a percentage. And be careful with the pictures you choose. In this case, I see a female dentist that doesn’t prefer Trident on Wednesdays…

You can animate, but only if you have a note from your doctor.

You can animate if there is a pattern inside (Hans Rosling, anyone?).

So, what do you think? Is Seth Goding ready for serious information visualization?

{ 0 comments }

Charts: Monthly Unemployment Rates by State 1976-2009

by Jorge

Here are two ways to display a relatively large dataset, montly unemployment rates by state since 1976. The first one is perfect to see the overall patterns, the range from the lowest to the highest, the outliers and the slopes. An interactive version would allow the user to highlight specific series.

A small-multiple version allows the user to focus on specific states, compare them to the normal band, etc. States are ranked by labor force size and, as you can see, in the first row seven out of ten are above the US average in April. In the last row, only one is above the US average. You can also see that Michigan was not well (unemployment-wise) long before the current crisis, or a spike in Luisiana (Katrina). It pays to study this chart carefully.

Bottom line: try to see the same data from different angles. There will always be semething interesting to find.

What do you think? How would you improve these charts? Would you use a different display? Share it in the comments! (here is the data file)

Update: I usually stay away from Excel’s surface charts, but I’d like to add this one:

Also check Michael’s Horizon chart.

{ 17 comments }

God and Moses? The Differences Between Edward Tufte and Stephen Few

by Jorge

I have a confession to make: my past is paved with chart-making sins, including some capital ones (yes, 3D pie charts, too). But years ago I saw the light in Edward Tufte’s The Visual Display of Quantitative Information and since then I’ve been avoiding eye-candy temptations. Now I do my best to pursuit the path of data visualization virtue.

Every God Has His Moses: Edward Tufte and Stephen Few

Some time after that first revelation, I stumbled on Stephen Few’s Show Me the Numbers and I though: “wow, Tufte for business!”. As a father of twins, I know that good things come in pairs, and now I had two great role models to help my recent conversion.

Or should I say one and a half?

Edward Tufte and Stephen Few are often cited together, as if they were a single entity. For many of us, simple mortals, Stephen Few is some kind of translator of God’s voice. Given Few’s background, that wouldn’t be completely inappropriate…

For some time that’s how I looked at Few’s work on charts and data visualization. But I was wrong. They do share similar views about basic data visualization principles. And they seem to share the same level of stubbornness, too. But there is a major difference.

Tufte, the Artist vs. Few, the Engineer

Tufte is an artist. His data visualization principles derive from Ludwig Mies van der Rohe’s minimalism, and in that sense, he approaches charts from an aesthetic point of view. His charts are as beautiful as a chart can be, if you happen to like the aesthetic minimalism.

I don’t know how and when Few became aware of the need for better data visualization. But he embraced Tufte’s principles not because he is an aesthete like Tufte, but because he values efficiency and those principles happen to improve it.

Stephen Few would never title a book “Beautiful Evidence”. He doesn’t mind to use Excel to create his chart examples, while Tufte needs full control of details like kerning (and he uses a designer’s tool, Adobe’s Illustrator).

On the other hand, Tufte would never write a book about dashboards (Beautiful Dashboards? brrrr…). From an actionable, business visualization point of view, Tufte is The Visual Display… Almost everything else is beautiful, yes, and perfect for the coffee table.

And while Tufte escaped Flatland for good, Few still keeps both feet firmly on the ground, discussing BI tools, pie charts or irregular time series (and I don’t think his new book changes that).

The Need for a New Business Visualization Model: the Emotional Link

Both approaches are very consistent and they give you a set of guidelines that you can apply to all your charts and adopt as a general framework.

What I am not comfortable with is their positivist attitude, specially in Few. Because Tufte’s charts are aesthetically pleasing, we can derive some emotion from that. In Few’s case, his charts are purely functional.

I still don’t know where to draw the line between purely rational/functional visualizations and the eye-candy. Let’s see this pattern:

Boy meets girl, boy gets girl, boy loses girl, boy gets girl back.

Do you feel emotionally overwhelmed? No? Do you even care about the story? Do you even care about the boy and the girl? Let’s try again:

John fell in love with Anna the moment she spilled coffee on his shirt.

This sounds much more interesting. Add three more sentences and you’ll complete the boy-meets-girl pattern. Both versions share the same pattern, but the second one adds some (perhaps irrelevant) detail and creates an emotional link between the audience and the characters.

You need that in data visualization, too. You don’t have to cry because you chart shows a market share drop in Alaska, but you must connect with the reality behind the chart and the data.

The Need for a New Business Visualization Model: Interaction

Jacques Bertin says that knowledge is built by the user when interacting with the chart. Why interaction (and animation) is absent from Tufte’s and Few’s books is something I don’t really understand.

Although I respect Tufte and Few, I feel that there are pieces missing in their theories. We can borrow some pieces from Bertin’s work (and Tukey’s?) and that will surely help, but the real issue here is to find the balance between the need to correctly (bureaucratically?) display the data and the emotional response that helps to keep the audience interested.

Back to you, a very simple question: what are Tufte and/or few missing? What pieces do we need for a XXI century visualization?

Photo credits: ~L. and David Zellaby.

{ 18 comments }

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

{ 5 comments }

← Previous Entries