Six Data Visualisation Best Practices

Your data is only as good as your ability to understand and communicate it. The best way to tell a story with your data is to visualise it through a graph, a chart, or another data visualisation technique. Data visualisation will help you uncover patterns, correlations, and outliers.  

However, it's critical to choose the right method of data visualisation; if your data is misrepresented or presented ineffectively, you and your team will lose out on key insights and understandings. This blog will lay out six data visualisation best practices to help you communicate your data more effectively. 

Collect reliable data 

Before you can visualise data, you need to collect it. Ask yourself where and how you are getting your data. If you’re collecting your own data, you’ll need to make sure it’s coming from a reliable source. A good data source is:  

Original – Your data should come from a primary source. If the data comes from a second-party source, such as Wikipedia or a news article, track down the original data to spot any mishaps.

Comprehensive – Your data should tell the whole story and leave no questions unanswered. 

Current – Ensure that the data is no more than two years old. 

Reliable – Verify that your source was relevant, legitimate, and unbiased. 

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Identify your audience and the story that your data tells 

After collecting your data, you need to look at the raw numbers and tease out what is significant. Start with your goal metrics: what is the primary question you are trying to answer? What is it that your audience is most curious about?   

The key thing to consider is what exactly are you trying to show your audience? Your aim should be to create a chart that demonstrates value and achieves its purpose in an easily recognisable way. Consider how familiar your audience is with the principles presented in the data and design your visuals in a way that allows your audience to process the data quickly and easily. Bar charts, pie charts, line charts, area charts, scatter plots, bubble charts, and heat maps all tell different stories – you need to choose the best one to tell your data story correctly.  

 

Decide what would you like to show 

Change over time 

A common use for data visualisation is to see how a variable changes in value over time. These charts usually have time on the horizontal axis, moving from left to right, with the variable’s values on the vertical axis.  

Data visualisations that show change over time include bar charts, line charts, and box plots.

 

Composition 

Sometimes it’s important to not only know a total, but also the various components that comprise that total. While other charts like a standard bar chart can be used to compare the values of the components, charts such as pie charts clearly demonstrate the part-to-whole composition. 

Data visualisations that show composition include pie charts, stacked bar charts, and stacked area charts.

 

Distribution 

Showing how data points are distributed is another important use for data visualisation. This is particularly useful when trying to build an understanding of the properties of data features. 

Data visualisations that show distribution include bar charts, histograms, violin plots, and box plots.

 

Comparison 

Another application for data visualisation is to compare values between distinct groups. This is a very common application and is often combined with other roles for data visualisation, like showing change over time, and includes charts such as bar charts, line charts, violin plots, and more. 

Data visualisations that show comparison include bar charts, dot plots, line charts, grouped bar charts, violin plots, box plots, funnel charts, and bullet plots.

 

Relationship 

Understanding the relationship between data features is another task that shows up in data exploration. Charts such as scatter plots, bubble charts, and heat maps can be used to plot two or more variables against one another to identify trends and observe the relationship between them. 

Data visualisations that demonstrate relationships include scatter plots, bubble charts, and heatmaps.

 

Provide context 

Ensure that you provide context to any data visualisation to help your audience interpret the numbers they are seeing. To provide context: 

  • Label your charts and graphs correctly  
  • Order your data sets logically – it is easier for an audience to understand a visualisation when the data is ordered intuitively 
  • Call out or highlight essential information – use arrows, text, or visual cues such as a circle or rectangle 

 

Tell stories with clear colour cues 

Colour has the power to deliver a message without using words. When using colour in your data visualisation remember to keep it simple. Use colour only to highlight and accentuate information, not to make numbers look pretty. Using too many colours can create discordance, while using only one colour or too many shades of a single colour can cause the data to blend together. 

Graphic showcasing analogous, monochromatic, triadic, and complementary colour schemes

Four common colour schemes are analogous, monochrome, triadic, and complementary.  

Analogous colour palettes consists of one main colour and two colours directly next to it on the colour wheel to create a softer, less contrasting design. This is better for an image than for data visualisation.  

A monochromatic colour scheme contains various shades and tints of one hue. This is great for when you don’t need to create high contrast or really grab attention. 

Triadic is a high contrasting colour scheme that retains the same tone. A triadic colour scheme is great for creating contrast, but can sometimes be overpowering. It looks best in bar or pie charts because it offers the contrast you need to create clear comparisons. 

A complementary palette is the use of two colours directly across from each other on the colour wheel and relevant tints of these colours. The high contrast helps you highlight important points and takeaways from your data. 

 

Keep things simple and digestible 

Coherence is essential when putting vast amounts of data into a visualisation. A coherent design will seamlessly deliver your data in a way that allows your audience to easily process information without it being too ‘in-your-face'. 

Remember that the order in which data is displayed, the colours used, and the size of various elements of a chart can help users interpret data more easily. 

Make sure your data visualisation tells a story clearly; avoid using visual representations that do not accurately represent the data set, such as 3D pie charts which can skew what your data  and confuse your audience.  

 

Remember:

Your data is only as valuable as your ability to communicate it. Follow these six data visualisation best practices to tell your story effectively. If you're ready to be a data-driven business, please contact me for a meeting.