Know your audience
Consider the audience’s level of:
- Literacy: You can use symbols, illustrations, animations and other universally understood graphics
- Numerical literacy: Even educated audiences are not always comfortable with data and stats. Do they understand ratios, complex formulas or statistics. Or do they need simplified data?
- Education/Level of technical expertise: Simplify content and define terms for less technical audiences but provide more detail for those with expertise
- Job function: What is the purpose of your data visualisation? Academics will want to know how data fits into existing literature. Funders will want to see results compared to money spent. An executive will want high-level results to guide decisions. A programme manager may only be interested in data relevant to their specific topic/area.
Find the story
Data Visualisation as storytelling
- Think of your data visualisation message as a thesis statement which needs a summary in a few concise sentences
- The ability to create a compelling, visual argument will be greater if you begin with a clear and focused message
- The type of story you tell affects the platform you will use.
- Infographics might be more useful for persuading the audience of your point of view, where dashboards leave the interpretation to the audience.
- Animations may be effective for college students, but not amongst older adults.
Use colour effectively
How to choose colours:
Step 1: Decide what the colours will represent
Decide which aspects of your data you want to represent with colour.
Step 2: Understand your data scale
The ColorBrewer tool defines three types of scale:
- Sequential – when data values go from low to high
- Divergent – when data has data points at both ends of the scale, with an important pivot in the middle.
- Qualitative – when the data does not have an order of magnitude.
Step 3: Decide how many hues you need
Based on the scale you chose in step 2, you can decide how many hues you need in the palette:
- Sequential data usually requires one hue, using luminance or saturation to define scale.
- Divergent data requires two hues, decreasing in saturation or luminance towards a neutral (usually white, black or gray).
- Qualitative data requires as many hues as values, but remember the limitations of the human brain. Try to not use more than seven or eight colours, otherwise the brain cannot recall what each one represents.
Step 4: Look for obvious options
Before getting too creative, take a look at your data to see if there’s an obvious set of colours.
Your application or corporate style guide might be a good starting point.
Step 5: Create your palette
Use one of the many web resources. ColorBrewer is one of the best for picking schemes for sequential, diverging and qualitative data. Or if you have a starting point in mind, Adobe Color creates palettes from a single colour.
There are several groups of colours that work well together. You can identify them by their relative positions on the colour wheel:
- Monochromatic – shades of a single hue (sequential data).
- Analogous colours – colours that sit beside each other on the colour wheel (varied alternative for sequential data).
- Complementary colours – from opposite sides of the colour wheel (diverging data).
- Triadic colours – 3 colours equally spaced around the wheel (good starting point for a qualitative palette).