Good data visualisation can help researchers and other users to explore datasets and identify patterns, associations and trends, and also to communicate that understanding to others.
Good data visualisations provide an effective representation of the underlying data that illustrates the answer to a particular question. They can inform non-specialist audiences and help decision makers make robust decisions based on the data being presented. Presenting data in this way can support strategic planning, performance monitoring or delivery improvement.
Good data graphics should:
- Make large data sets coherent, and encourage the audience to make comparisons between different data sets
- Reveal the data at several levels of detail, from a broad overview to the fine detail.
- Avoid distorting what the data has to say
- Present many numbers in a small space - but also emphasise any significant numbers
- Help the audience think about the important message(s) from the data, rather than about methodology
Principles of a good visualisation
Good data visualisation is simply another way to communicate with your audience, and the same principles of good communication design applies to data visualisation as with other ways of communicating;
- Identify your key audience;
- Set out the data accurately;
- Identify the point(s) that you want to make; and
- Create the clearest visualisation that conveys that message, using that dataset to that audience.
Design for your audience
Think about the message that you are trying to convey to your audience with your visualisation - and focus on emphasising this message. Try not to cram too many key points into the visualisation.
Different audiences may need different visualisations. An appropriate design for a visualisation to be used by researchers exploring how economic indicators vary over time and between places may not be appropriate when showing the same data to non-specialists in order to emphasise key economic trends.
Accurately represent the data
The visualisation should show the underlying data without distortion. For example, axes should always show zero to avoid exaggerating the importance of differences between data values.
Clear, detailed and thorough labelling should be used. Write out explanations of the data on the graphic itself, and label important events in the data.
Don’t try to do everything with one visualisation.
Organise the information in order to emphasise what you are trying to say to the audience. Don’t bury the key messages in a mass of detail.
Set out key points on the graphic itself, and label any significant or anomalous data events - the graphic should speak for itself.
Keep it clear
The graphic should focus on the message(s) for the audience, and all visual clutter kept to a minimum. But don’t cut out all visual elements - things that emphasise the key message are useful if they help get your points across to the audience
Reduce and refine. Keep asking yourself whether your visualisation suffers any loss of meaning or impact for the audience if an element is taken out.
The next post looks at some practical steps for good visualisations.
