Role Of Colour Palettes In Data Visualizations
By Kamal Jacob
An image is worth a thousand words and it makes literal sense in data visualization. Data visualization is the practice of converting massive out of complex data into the most efficient, visual forms such as graphs, maps, charts, and complex dashboards.
The human brain has a much harder time understanding data encoded in the form of text and numbers. However, visually displayed data is easier to understand, grasp and analyze making it easier to find patterns and even understand difficult concepts. Data visualization can improve storytelling and reduce response time helping businesses make a faster decision. Also, by breaking the most complex data into an easy-to-understand format, it even helps businesses make use of data that was previously regarded as unused.
Factors Influencing Data Visualization
There are many factors that can influence how a visualized data is processed by the audience. It is not only about using any data visualization software and choosing templates for graphs, maps, etc and let data do its task. Understanding the factors that affect data visualization is the key to having more effective storytelling which can correctly communicate the information you want.
And if you are thinking that selecting between graphs, pie-chart and other visualization elements are the only factors affecting data sets, you can’t be more wrong. The selection of the right colour palette can make a huge difference in the way a given data set is actually perceived.
Image Source: https://blog.graphiq.com/finding-the-right-color-palettes-for-data-visualizations-fcd4e707a283
Colours and The Psychology Behind Them
Colours can invoke a whole lot of different emotions in humans. From optimism to excitement, confidence, to anxiety, fear, and boredom. The emotional impact of colour in a person is dependent on the cultural conditioning as well as some universal human perceptions. In fact, different shades of the same colour can sometime convery vastly varied emotions.
And this association of human emotions to colours has been largely used by marketers to influence the potential buyers. One of the best examples of this is the use of colour blue. Blue is considered to be covey calmness, trust, and dependability. That is the reason why most financial industries such as Allstate, JP Morgan, Progressive, American Express, etc use blue in their brand. Also, the primary colour of Facebook is also blue. And what could be more appropriate colour choice for a brand that has connected the whole world into one?
RED, on the other hand, is considered as a symbol of love, passion, excitement and raw energy. It is the colour that screams attention. Therefore, most fast food and coke companies, including Coca Cola, Pepsi, Pizza Hut, McDonald's, KFC, all have red as their primary colour.
In data visualization, colour is very important to set the tone for the underlying data sets. Of all the design elements in data visualization including the graphs, the headings, the analysis, etc, colour is probably the most important element that can turn a modest visualization into an emotion-filled data story.
Data visualization is very ‘Visual’ and therefore it is important to use all the elements including colours to represent your data and attract attention to the right bits.
How Colours Can Simplify Complex Graphics?
Colours used in data visualization has a very subtle yet powerful effect that can define your judgement and perceptions. If you have a decent amount of data to convert into visualization, it can easily turn into visual clutter with countless lines, graphs, and maps. By using contrasting colours or a set of different colours to represent a scale, you can simplify complex graphics. For example, if you are comparing two data sets, you can use two contrasting colours (such as blue and orange) to help viewers see and figure out the difference.
In addition, using different colours to convey different meanings can convert dull and uninteresting data into self-explaining images. For example, in the given graph it shows that starting from 2003 the deaths from AIDS in South Africa have been higher than normal deaths and have increased dramatically in the following years.
Image source - https://curethefear.weebly.com/charts-and-graphs.html
Here the death from Aids have been shown in red colour whereas the normal death is depicted in green. This is because green depicts positivity and red is the sign of danger. Now imagine if we reverse the situation and show death through AIDS as green and normal death in the graph as red. Will it have the same impact as the given graph? Definitely not. Because in this image, colours portray emotion and using the right colours to present the information is important to get your viewers feeling the way you want them to feel about your data.
How to Improve the Use of Colour in Data Visualization?
Using varied colours to differentiate between information and patterns in a graph is important. However, you cannot just throw around colours haphazardly assuming that it will invoke the feeling you want in your audience. By doing this, you risk creating a visually chaotic data set that can confuse them rather than simplifying the data. Here are a few tips you can follow to improve the usage of colour during data visualization:
Use a contrasting and dynamic range of colours
When creating a graph or a pie chart or any other data element, you want your viewers to be able to easily distinguish between two data sets. For that, it is important that the colour pallet used should be easily distinguishable. The colours should be contrasting and should vary enough in brightness to be distinguished effectively.
For example, in this graph different colours are used to depict different micronutrient’s deficiency. The colours used are contrasting making it easier to read the data.
Use the same colour for similar information
Many times a certain variable is repeated across different data sets. In such cases, you should be careful enough to use the same colours for them across data sets. This makes it easier for viewers to recall the information and find patterns in the data (if any).
In the graphs given below, different tones of the same colour are used to show the level of deficiency. And although the same visualization type (map) is used several times in the row, it doesn't look repetitive. In fact, it helps the user to relate to the information and guides the eye.
Colour of the background is important too!
You have used a wide gradient of colour in your visualization only to realize that viewers couldn’t differentiate between them when projected. The culprit? Background colour. Selecting the right background colour is as important as the colour palette for the data set. Great contrast between data colours and the background colour make it easier to read and understand the visualization. If the text size is really small in the data set, special attention should be given to creating greater contrast of the test colour against the background colour.
Also, many times when a data set is projected on a bigger screen, the colour gets diluted and may look totally different from what it appeared on your computer screen. Therefore, if you are making a presentation, make sure to test it using a projector beforehand.
Use appropriate colours depending upon the data you are presenting
As already mentioned in the example of AIDS-related death, it is important to use appropriate colours based on the data. Similarly, when indicated a scale of low to high, use light colours to represent the lower value and move to a darker tone for higher values. It holds especially true when drawing a finance-related graph or a heat map.
Colour is a very important part of data visualization. It enables effective storytelling, one that engages the viewers at an emotional level and captures their attention immediately. A well-chosen colour pallet can reduce the time for understanding and hence for action helping businesses make quick decisions.
Getting the right colour for your data visualization needs both understanding of the colour as well as practice to implement it. Every time you create a visualization using data set, take a critical look at it assessing whether it is communicating your message effectively. Make right your use of colours to put your point across and level up your data visualization
And that’s a wrap. Hope you liked the article and found it insightful. Also, check out our data visualization courses to upskill your data visualization skills and get 360-degree learning of the subject.