During week 38 we asked the community to visualize a short and simple dataset about train versus plane travel.

I noticed a lot of enthusiasm and increased participation from the community, which is encouraging and the challenges resulted in a number of great vizzes across the board.

Particularly during Viz Review I noticed a few submissions which may not necessarily stand out visually compared to some others but which had such great insights and thorough analysis that I was reminded that it’s so easy to overlook some people’s work and miss out on excellently researched and executed data stories.

A couple of clear lessons emerged for me this week.



One thing I noticed were a number of submissions which in their title talked about costs or ‘which is cheaper: train vs plane’ but then went on to show costs, carbon emissions and travel times. The content wasn’t clearly linked to the title – or the title didn’t accurately reflect the actual visualization.

If your title is just about the monetary cost, e.g. by asking which mode of transport is cheaper, then make sure your viz very clearly focuses on that.

A title that isn’t consistent with the rest of the viz can be confusing for your audience and might also makes people wonder whether you understood the topic when you created your viz.

If someone buys a book with the title ‘simple home cooked meals’ and the actual content is all about gardening, then they’re disappointed, so let’s make sure these things don’t happen with our visualizations.

When you finish your work, go back and check whether the title really matches the rest of the viz.



Numbers can be misunderstood when it is unclear what they represent. There are many options for labelling your charts to ensure the metrics you’ve included are clear. You could describe your metrics in the subtitle, the title, an annotation, on the axes or with labels in the charts themselves.

Ideally, things are clear and possibly even self-explanatory. Sometimes we have to make a bit of extra effort to ensure our visualizations are easy to understand.

Experiment to see what works best for your viz and particular design. If you want very minimalistic charts, then moving the labelling to the subtitle is a great way to go. If you want to guide your audience through a data story, then annotations directly in the charts can be helpful.

What’s most important is that your audience can easily and quickly understand what the numbers refer to.