How good is your data?
- Make sure your data is clean.
- Check for duplication, missing data or information.
- Remember - Garbage In, Garbage Out.
- Is your visualisation open for misinterpretation due to bad or incorrect data?
Are you using the right kind of visualisation for your data?
- Consider what type of visualisation is best to convey your message.
- Think about your audience - will they understand the type you choose?
- What are you trying to show? e.g. relationship, comparison.
Static vs interactive?
- Do you want your data to be explored?
- How are you using your visualisation? e.g. presentation, website, journal article.
Be aware of clutter
- Is all the included information adding value? If not, remove it.
Is there too much information?
- Don't try to include everything in one visualisation. Consider using multiple ones.
- Be clear about what you are trying to say.
- Focus on a specific point or option. This can help increase understanding2.
How have you used colour?
- Limit number of colours used. Are they causing confusion instead of clarity?
- Don't choose a colour just because of aesthetic reasons. Could the colour have an unintended meaning e.g. due to cultural reasons?
- It should have a purpose eg. highlight something, show related data.
- Don't use similar shades when trying to show contrast.
- If using a fill colour, use a dark shade.
Are the scales used inconsistent?
- Ensure the scale of multiple visualisations are consistent unless you want to indicate importance or emphasis.
Who is your audience?
- Don’t be too general or try to reach too many people.
- How are you perceived by your audience? Are you seen as a credible source for this information?
- Use different visualisations for different audiences.
- Consider what tone eg. serious, celebratory would be appropriate.
Labels, headings and text
- Are your labels providing clarity or confusion?
- Do you need to add labels or headings to help communicate your data?
Use of contrast
- Lack of contrast can make data less easy to understand.
- To highlight something, make it very different eg. shading.
Be careful with alignment when using text
- Make use of clean lines.
- Try not to use too many diagonal elements.
Test It Out
- Get feedback e.g. audience sample, colleagues, peers.
- Fresh eyes can provide different perspective.
- Is it communicating your data the way you want it to?
Sources 3, 4, 5