Visualising data II

Practical steps for good visualisation
In my previous post on Data Visualisation, I highlighted the four key principles for good visualisation:

• Design for your audience: Think about how to emphasise the key point(s) that you are trying to convey to this audience with this particular visualisation
• Accurately represent the data: The visualisation should show the underlying data without distortion, and avoid common pitfalls that obscure the real information.
• Organise the information: The visualisation should have a clear purpose - communication, exploration, tabulation or decoration.
• Keep it clear: The visualisation should focus on the message(s) for the audience, and all visual clutter kept to a minimum (except where useful to highlight key points).

What does this mean in practical terms? For each principle there are a number of basic steps that can be taken to improve your data visualisation. Some of these are straightforward to implement, for example ensuring that you are not using decorative effects that hide the data. Others require more work, for example testing your visualisation with key audiences.

Design for your audience   

• Test your visualisation with your key audience
• Know when to use dynamic tools, when to use charts, and when to use tables
• Limit the number of categories shown in a visualisation - be selective in what you present in order to emphasise the key message(s)

Accurately represent the data   

• Don’t distort the scale to give undue weight to statistically insignificant data 
• Keep the zero on the axis scale
• For bar-charts, set the base of the bars to zero (not the lowest value)
• Avoid varying the size/area of objects in graphs, except to convey difference in values
• Avoid using line charts where data is only available for a small number of data points

Organise the information

• Bar graphs are good for showing how data changes over time.
• Pie charts are visually simple and easily understood, but can be manipulated to give a false impression.
• Scatter graphs or line graphs are used to investigate the relationship between two variables, providing sufficient data points are available.
• Bubble charts or triangular graphs can be used to show how the relative dominance of one or more factors combined can influence direction of travel.
• Radar or kite charts are good for comparing multiple factors for different options.
• Choropleth/Isopleth maps show areas shaded according to a prearranged key.
• Treemaps display hierarchical (tree-structured) data as a set of nested rectangles.
• Sound and motion can be used to show changes over time, or changes based on dynamic variables.

Keep it clear   

• Avoid using purely decorative effects such as 3D that can hide the data
• When choosing a colour palette, limit the number of colours used and ensure that different colours can be distinguished from each other
• Where colour is needed, use solid blocks of colour and avoid complex fill patterns
• Avoid using strong or bold colours for the background in a visualisation