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).
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.
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).
(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).
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?”.
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.
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.
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.
What we see as detail can be seen by someone else as clutter. Clutter is the natural child of loss aversion and 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).
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.
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.
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.
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 want to learn about interactive charts my Excel dashboards may be a good starting point).
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?
[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…]