Practically every movie that comes out now is available in 3D, featuring the obligatory scene where it looks like a piece of something-or-other from an explosion is headed straight at you from out of the screen. 3D HDTVs have been available for a few years now, allowing you to wear the giant idiotic-looking 3D goggles in the privacy of your own home (although that particular 3D wave seems thankfully to be receding without having made much impression). Sometimes, film 3D actually works quite well and appears seamlessly and goes nearly unnoticed when it’s done perfectly. Most often, however, it feels forced and was done by producers just because they could.
While 3D works at a degree in the world of entertainment, when your primary objective is visualisation and communication of BI or market research data, it by and large doesn’t work.
The proliferation of easy-to-use dataviz software on the market means that more and more people are able to produce and publish their own charts. That’s great, but it ultimately means there will be many examples of not just “bad” charts, but charts that are totally misleading in what they present. One aspect of this which is a particular bête noire in the dataviz community is the 3D chart, and in particular, the 3D pie chart.
People typically create 3D charts simply because they can, and believe (often mistakenly) that they’re more “sexy” than “flat” charts. We can recall, during our formative years in MR, using a great charting package called Harvard Graphics. It was so cool and revolutionary when it came out, and yes, we too produced 3D charts (*we collectively hang our heads in shame*). However in today’s world the communication of information through data has become increasingly mission-critical, and so we need to ensure that the message we are conveying is utterly accurate.
Let’s look at an especially egregious example….
At first blush, the headline stories that we may take away from this chart are
- The majority of sales took place in the East (I mean, look at the SIZE of that wedge!)
- Sales in the East were roughly triple those in the West (bad job, West!)
However, if we display the actual value labels to this chart we see a different story…..
Now we can see that
- Most sales were in the North (well done, North!)
- Sales in the East were only double those in the West (still, c’mon, West!)
So you can see how just by attempting to sex up the data by adding 3D perspective to the chart, we’ve totally changed the message the data conveys. In fact, if it were a normal 2D pie-chart, the correct message would be conveyed without ambiguity as shown below
The takeaway should be to keep it simple, because extraneous “noise” like 3D can easily distort the data’s message.