Visualisations are the new black

This is the 3rd in a series of 3 articles:

First we looked at Colouring with Numbers – can data present a better picture

The second showed how a story is worth a thousand visuals

Now we conclude with how Visualisations are the new black

 

In my last blog “A story is worth a thousand visuals”, we discussed how to consider the layout of the report to entice and lead the audience through to find information that is of importance.

Now that we have gotten the audience to this point it would be all for nothing if they can’t effectively interpret what they are seeing. This is where the choice of visualisation to present this information is paramount. Choose the right visualisation and the audience can understand and interpret the information clearly. Choose the wrong one and the information can become lost or misinterpreted.

“So how do I know that I have chosen the right visualisation?”

Glad you asked.

You won’t.

No matter how you believe the information should be displayed, it’s ultimately up to the audience that you are delivering to that will determine if what you are portraying is effective.

To assist with trying to get a visualisation that is effective as possible these are the five rules that I use to help achieve this:

Rule 1 – “Always consult with your audience”

You will always be closer to the data than your audience and you will naturally use this to establish your own beliefs around the correct way to represent information. If you consult with your audience, they will assist in helping to ensure you maintain an objective view. If you can’t consult with your audience, try at least to seek an independent reviewer. If you find you are having to explain what they are looking at, this is probably a good guide that the visualisation you have created isn’t achieving its intended purpose. Remain objective and open to critique. Everyone perceives information in different ways and you have to remember that this needs to be received by an audience that may not see the information the same way as yourself.

Rule 2 – “Understand what it is you are trying to say”

If you can’t understand what it is you are trying to visualise, how can you effectively translate it into something that can be understood by others?

Before choosing any visualisation, stop and take a minute to ensure you have formulated the question to which this visualisation is going to provide the answer.

Always check your understanding of the true intent of the question. Interrogate further when required. Make sure the breadth and depth of what is being requested is captured ensuring the specific detail actually sought can be represented within your visualisation.

Along with understanding what it is you are trying to visualise, check that the data that you are using is accurate and correctly represents the question and answer.

Rule 3 – “Choose an appropriate visualisation”

The goal of data visualisation is to communicate information as efficiently and clearly as possible to an audience to enable analysis and understanding. It tries to reinterpret complex data to make it more accessible and as much as the interpretation of data is a science, the presentation of the data is art.

Edward Tufte, a noted leading figure in data visualisation, wrote in his book “The Visual Display of Quantitative Information” the following principles for effective visualisation:

“Excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphical displays should:

  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production or something else
  • avoid distorting what the data has to say
  • present many numbers in a small space
  • make large data sets coherent
  • encourage the eye to compare different pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear purpose: description, exploration, tabulation or decoration
  • be closely integrated with the statistical and verbal descriptions of a data set.

Graphics reveal data. Indeed, graphics can be more precise and revealing than conventional statistical computations.” (Tufte, 1983)

So where do we start?

A good guide in determining what chart type helps to present what type of information was developed by Dr Andrew Abela shown below in Figure 1. It’s based around the four analytical models of comparison, composition, distribution and relationship.

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Figure 1 (Abela)

This provides a great starting point for choosing a visual representation of the data. But remember to use this as a guide. Always assess if the visual is staying true to what you are trying to present.

Some good resources to assist further with understanding types of visualisations include:

www.datavizcatalogue.com

http://labs.juiceanalytics.com/chartchooser/index.html

http://annkemery.com/essentials/

 

Rule 4 – “Use colour to enhance the visual and not detract”

Colour can be a powerful tool to draw your audience in and focus their attention. But it has to be used with care as it can just as easily detract and cause confusion. Once again Edward Tufte provides us with some guidance here:

“…avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm.” (Tufte, Envisioning Information, 1990)

We process colour before we are even consciously aware that we are interpreting it and we can use this to our advantage when presenting a visualisation to provide the audience with clarity and direction on interpreting the information.

Colour should be reserved in its use and only applied when it adds meaning to the data to do so.

For example, presenting the following two column charts. Both are displaying exactly the same information

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Figure 2

f3

Figure 3

The chart in Figure 2 is harder to interpret than Figure 3 due to the selection of individual colours for each column. When the audience sees Figure 2 they instinctively try to apply a meaning to the colour scheme. It is better to remove this mental fatigue and use a singular colour as in Figure 3 as the audience will interpret this as all the same data with the comparison to occur at the individual column.

Try and use soft colours predominantly reserving more intense colours for drawing attention to specific points of interest.

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Figure 4

f5

Figure 5

As shown in Figure 5, increasing the lightness of the surrounding colours allows the intended data point to be drawn in to focus in comparison to the same information represented in Figure 4.

In concert with trying to highlight the relevant data, helper information such as axes, data labels, background colours and borders should be muted so as not to detract from the information being presented. Figures 6 – 9 below show some examples of how this may look when not taken into consideration.

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Figure 6

f7

Figure 7

f8

Figure 8

f9

Figure 9

To ensure consistency and cohesiveness throughout your visuals, establish a colour palette that you can use. The palette should enable you to display data that is of the following types; sequential, diverging and categorical.

Sequential colour palettes are used to organise quantitative data from high to low using a gradient effect. You are generally wanting to show a progression rather than a contrast. By using a gradient-based colour scheme this allows you to show this progression.

f10

Figure 10

Diverging palettes show information that moves outward from an identified central point of the data range. A typical diverging palette uses two different sequential palettes so that they diverge from a shared light colour toward dark colours at each extreme but provide a natural visual order that assists the audience in interpreting the progression.

f11

Figure 11

Categorical colour palettes are used to highlight categories of data. With categorical data, you typically want to create a lot of contrast to ensure visual distinction between each category. To do this use different hues to represent each of your data points.

f12 

Figure 12

After establishing your palette ensure you include complimentary colours where the brightness is reduced to enable you to use colours from your primary palette to highlight and the secondary palette to support. If we were to do this with Figure 12 it would create a palette as shown in Figure 13 below.

f13

Figure 13

Fortunately, there are many websites that can assist you in establishing your colour palettes without having to have an intimate understanding of colour theory. Some recommendations I can make are:

http://paletton.com

http://colorbrewer2.org/

http://tools.medialab.sciences-po.fr/iwanthue/

http://www.colorhexa.com/

https://color.adobe.com/create/color-wheel/

A final word on the use of colour wouldn’t be complete without recognising accessibility requirements. Approximately 10% of males and 1% of females suffer from poor colour perception, commonly referred to as colour blindness. It is recommended that designing your palettes that the colours you choose should accommodate for this. Colorhexa has a good visual tool to assist you with understanding how a colour is perceived by the different types of colour perception.

Rule 5 – “Ensure clarity in your visualisation”

When designing your visualisation, remember that the key is to communicate information as clearly and quickly as possible. Only visualise information that is relevant and enhances what is being interpreted.

Now that you have constructed your visual, stand back and look at it. Squint. Is there anything that detracts or confuses the information you are trying to present?

An example of how too much noise can cause confusion is illustrated in the example below in Figures 14 and 15.

In Figure 14 the American Joint Economic Committee, Republican Staff released a chart to demonstrate the complexity of the American Affordable Healthcare Act.

f14

Figure 14

An American citizen, Robert Palmer, felt that the chart was purposefully designed to highlight what is a complex topic by making the chart itself difficult to read. Thus, he redrew it as shown in Figure 15 to demonstrate while still a complex topic clarity of information could still be presented.

f15

Figure 15

(Palmer)

TL;DR

In summary, if you have skimmed to the bottom of this looking for the quick answers here’s my rules for visualisation:

Rule 1 – “Always consult with your audience”

Rule 2 – “Understand what it is you are trying to say”

Rule 3 – “Choose an appropriate visualisation”

Rule 4 – “Use colour to enhance the visual and not detract”

Rule 5 – “Ensure clarity in your visualisation”

 

References

Abela, D. A. (n.d.). Charts. Retrieved from Extreme Presentation: https://extremepresentation.com/design/7-charts/

Palmer, R. (n.d.). Retrieved from Flickr: http://www.flickr.com/photos/robertpalmer/3743826461/

Tufte, E. (1983). The Visual Display of Quantitative Information. Cheshire, Connecticut: Graphics Press.

Tufte, E. (1990). Envisioning Information. Graphics Press.

 

 

 

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