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Best practices for presenting your data

Article about best practices to create attractive data visualisation
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Presentations are an important part of the research process and allow the researcher a chance to illustrate the heart of their research to a public audience. The use of graphs in presentations is a powerful way to portray the narrative that the data analysis is representing. However, there are a few tips and guidelines that every researcher should consider when using graphs in public presentations. Let’s go through a few of them:

Less is more

It is important not to overwhelm your viewers with busy graphs that are difficult to read and complex to interpret. When presenting your work, simplicity is often the best way to go as it helps prevent people from being distracted by the graphs and not listening to you present the content.

Highlight key data points

We all understand that as researchers we are proud of the data we have generated and analysed, but often there is too much data to consume in one presentation. When presenting, try to summarise the data and discuss the most important or exciting data points.

Be careful when displaying sensitive data

Sensitive information and data are often necessary and vital parts of the data generated in the research you may be undertaking. However, it is crucial that you, as the researcher, make sure that sensitive data is protected and anonymised, as well as consented to by those the data would impact.

Eliminate redundant labels

Clutter around your graphs makes it difficult to read and observe by the audience listening to you. So, it is important to eliminate unnecessary labels and data points that have no bearing on the message that is potentially being portrayed.

Choose the charts that best tell your story

For example, a pie chart should only be used when you have two categories, or if you really want to push it, up to 6 categories. This is because a pie chart leaves it to the user to find the comparisons. Take a look at the example in Figure 2 (left) below, which shows an example of when a pie chart fails.

Therefore, when multiple categories are compared, it may be more effective to use a bar graph (Figure 1, right). Similarly, when you use a line graph, the reader will expect to see changes over time. Histograms, on the other hand, should be used to show where your data is clustered. You will find more examples on when to use which graph in this article from Tableau.

Decorative illustration comparing a pie and a bar chart, emphasising that for 4 categories a bar chart is more visually appealing

Click here to enlarge the image

Figure 1 – Here the pie chart has too many categories, but the graphic on the right uses both colour intensity and a bar-like approach to quickly summarize the results of social customer service. Source: Column Five Media

Include a baseline

It’s common to find a graph which does not start at zero. “So what!”, you say. One can still see when one value exceeds the other. That is true, but as shown in Figure 2, these values may be grossly exaggerated when we don’t see the start line. Similarly, you may find a graph giving you a bunch of fasting blood sugar levels for school kids. How would you know which values are good/bad without a baseline stating that it should be below 99 mg/dL?

Illustration showing truncated axis distortion, which leads to message exaggeration/understatement type of deception

Click here to enlarge the image

Figure 2 – Without a baseline, the interpretation of the data may be over-exaggerated. Source: Chad Skelton

Use colours meaningfully

Colours are a crucial part of the graphs used to present your findings, as they are a powerful visual aid that will help your viewer easily interpret and remember the data. However, colour can also completely ruin the way you represent your data and become a stumbling block for your viewers. Firstly, choose a colour scheme with more neutral colours, as brighter colours are more likely to clash with others. Secondly, it is likely that some of your audience has some form of colourblindness, so you should consider the impact this may have. You could avoid certain colour schemes (the most common form of colourblindness is for red and green) and/or ensure there is sufficient differentiation between shades that colourblind people would nevertheless be able to appreciate the difference.

Choose colours that allow the text colour to be easily read; you can change the text colour to white rather than the standard black, but it should be consistent. Similarly, think of the background colour of the presentation the data will be displayed against. It’s usually good practice to use solid or matte colours, rather than patterned effects. If using a gradient colour scheme, e.g. all data is in different shades of blue, then ensure there is sufficient differentiation between each gradient.

Think carefully about colour associations the audience may make. For example, it may be confusing to have negative data shown in green while positive data is depicted in red.

In general, try to keep a consistent colour scheme throughout your presentation to make it easier for your audience to follow.

Avoid using special effects

Avoid using excessive special effects, including dramatic slide transitions or animations, as they can be distracting and, in some cases, very unprofessional. Be wary when using special effects and only use them when they are truly necessary for the graphs.

Make sure you use the right text and fonts

One of the most overlooked aspects of a graph is the chosen font, as well as its size. We often want to have a cool and funky font to use for our graphs, but these fonts must be readable and professional. So, when choosing a font, think of what the head of the research group would be happy seeing in your presentation.

Use white backgrounds

Try not to use colour backgrounds for graphs and use white backgrounds whenever you are adding graphs to your presentation. This is to ensure that all values and illustrations in the graph can be read and viewed by your audience.

These are just a few tips and guidelines that will hopefully aid in you creating the best presentations.

Let us know in the comments how you might change how you display your data for a live presentation, compared to a written report or scientific publication?

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