I’ll be the first to admit that I am terrible at colours. Be it the selection of paint for a room through to the colours of an Excel chart. I simply choose the ones that I liked without much regard to everyone else. It’s natural for me to think “well I know what I’m talking about or looking at”. What I often forget is “what will the end consumer of this think?”. We all know that data visualisation brings data science into a consumable format for end users and dramatically helps us humans to interpret information easier and quicker.
Take the following table for example:
If we wanted to compare Domestic with International sales and find the peaks and troughs in the data we would need to read each line and work it out in our head. Although not an arduous task, it still takes time to interpret and calculate in our heads.
But if we visualise that same data we can quickly see this information:
— “Maps were some of the first ways that the human race looked at data in a visual format. —
But an often-overlooked component of data visualisation is the colour aspect. Do it right and the results will speak for themselves and your work will be well accepted by the business. But get it wrong and it can lead to confusion and misinterpretation. Heaven help us that all our hard work in data wrangling, sorting and analysing all comes to nothing just because we chose the wrong colour for a data value.
So why is colouring so hard to get right? The answers are quite simple
Cultural interpretation of colour – If you see a red light you stop, stop signs are red, warning sirens are generally red. So as a result you generally accept red being a colour of danger. But this isn’t necessarily so in other cultures as the colour red in China means prosperity and luck. So think twice about colouring negative values in red if you’re end user’s are Chinese.
Colours are hard to tell apart – How often have you been stuck trying to find a different shade of blue, brown or a pastel colour? Representing different values in similar colours often leads to confusion resulting in the consumer having to refer to a legend to understand what colour value matches the colour they are looking at. Worse still is those who are colour blind may interpret a result entirely different as the colours are so close to each other that telling them apart becomes impossible.
Apart from employing a visual designer to ensure your data visualisations are top notch there are two very simple rules to keep in mind. Of course there are numerous other rules and there are volumes of thesis written on this very topic, but two simple rules listed below are a start if you are like me and suffer from a lack of colour skills in the design stage.
1) Colouring sequential data
Sequential data is data that progresses from low to high or high to low and therefore you should use gradient colours to represent the change in gradient. Once again it is a fine line of colours you use here as you want to ensure the colours are distinct enough to represent the gradient curve, but not so distinct as to represent dramatic changes in values. Stick to colours from the same colour group.
2) Colouring qualitative data
The opposite of sequential data is qualitative data that represents categories that are distinctly different from other categories on the screen. They want or need to be seen as totally different from others. This where you need to apply contrasting colours to highlight the differences. For example green against a blue.
Always keep the end consumer of your visualization in mind. You may know your data inside/out and therefore understand it, but until someone who doesn’t know your work looks at it, you’ll only be designing from your perspective.
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