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Data visualisation

Basics of data visualisation - what it is, techniques, how to create, tools & software.

What to consider

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? eg. relationship, comparison.

Static vs interactive?

  • Do you want your data to be explored?
  • How are you using your visualisation? eg. 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 eg. 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 clean lines.
  • Try not to use too many diagonal elements.

Test It Out

  • Get feedback eg. 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

The Dos

The Don'ts

Colour blindness

Colour blindness (or colour vision deficiency, or CVD) affects about "8 percent of men and 0.5 percent of women"6. The most common type is "red/green colour blindness". It makes it hard to decipher colour coded information, such as used in data visualisations.

Adobe Photoshop or Illustrator

  • Can use Proof Setup tool to simulate how someone who is colour blind would see a visualisation.
  • To access, go to View > Proof Setup > Color Blindness

Useful resources