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Common Methods of Data Visualisation

This article from our strategy consulting team provides a useful background to the many different ways a business typically uses data. Identifying these requirements in your organisation is a key first step in developing your data strategy. Use our list as a starting point for your own as you embark on your data strategy journey.

What is data visualisation?

Data visualisation is the process of bringing the results of your data analytics and data science solutions to life, through the process of data storytelling and visual aids such as graphs and infographics.  Too often data visualisation is an after-thought in the analytics process, but it's really incredibly important, as it plays the key role of communicating the businesses findings and recommendations.  All the good work a business completes will come to nothing if it is not able to explain and communicate the data effectively.

The options for communicating business analytics are growing, with more and more automated data visualisation solutions available and integrated into analytical tools.  These tools now offer incredibly rich functionality and this is making it much easier to produce great results, without the specialist help of graphic designers.  However, it also means it has become very easy to overdo things and the message can easily become lost.

Why is data visualisation so important?

Data visualisation is an absolute necessity in all forms of business communication, in order to ensure that the audience understands what the business is telling them, remembers it and acts upon it.  This is down to a simple matter of human biology.  Our brains are designed to be visual and as such we naturally work much better with imagery than we do text.  In fact the human brain is thought to process images 60,000 times faster than text, which is unsurprising when we consider that 90 percent of information transmitted to the brain is visual.

What are data visualisation techniques?

There are a huge array of data visualisation techniques that are readily available in most analytical software packages.  Some of the main techniques include:

  • Traditional Graphing:  Line, bar, pie and column charts are all techniques many of us are pretty familiar with, as they have formed part of the school curriculum for us all.  With this comes a certain intuitive ability to read them relatively easily, so long as they are being used properly.

  • Number Charts: These are great for calling out a key number that a business would want their audience to notice. Up and down arrows or + or – signs can be added to show growth or incrementality.  These are being widely used in many consumer apps such as out fitness trackers and so are also pretty well understood by most audiences.

  • Maps: These are great for showing geographic distribution and can be used with the map as an integral feature by colouring different countries to represent different things or with additional visualisations such as column charts as an overlay.

  • Gauges:  These look like the speedometer on a car's dashboard and can be a good way to show progress to target or between an upper and lower limit.  However, they need to be used with care, as of they are overdone they can look a bit gimmicky.

  • Infographics:  These can be an excellent way to summarise a whole set of information.  They can visually direct the reader through the story and combine simple to understand imagery and text.  Infographics can be a powerful yet playful way to get a message across.

Does data visualisation work?

Visualisation can be a massively powerful piece in a businesses' toolkit to deliver effective data analytics.  However, to make it work successfully for a business, it is important to recognise that effective data visualisation requires storytelling skills.

 

The power of data to drive business growth can be immense, but this opportunity can all too often fall at the first hurdle because the decision-makers within the business follow their gut over the cold hard facts presented by the data. More often than not this is because they do not fully understand the story behind the data.

Numbers need more to them than visualisations alone to come to life - they need a story. Data storytelling is a key skill required of insight and strategy teams if their work is to have any legs. Data storytelling combines data visualisation, great narrative and importantly, context.

We are all human and while many won’t readily admit it publicly, we find even the simplest of data sets on their own hard to read.  This is because, we weren’t taught to instantly recognise patterns amongst numerical text. Adding a simple visualisation alongside the numbers e.g. a line chart, enables us to see so much more with little to no effort.

These visualisations bring the numbers to life and we can start to see trends and patterns.  If a business can then add on the narrative behind what they are looking at and why it is happening, the data story comes to life and has meaning.

All too often, analysis presentations become a series of chart after chart with a commentary running along the top, but this misses some of the crucial elements that the mind needs to grasp what it is being shown. Storytelling needs data, visualisation, narrative and context working together, if the organisation is going to really get the message across.

How to make a great data visualisation

​Things to think about when creating a data visualisation:​

  1. Know your audience:  Understand the audience and their appetite for information breadth and depth.  Some people need to see the big picture first, before being taken down into the detail.  Likewise some people are more used to looking at data all the time - finance teams, analysts, while for others it is more of a challenge. Make sure to adjust the level and the detail of what is being displayed to suit the end user. 
     

  2. Define the objective of your visualisation:  What do you want the audience to take away from it?
     

  3. Select the right chart type/s:

    • Column: Categoric data.

    • Clustered Column: Compare multiple categories of data within individual sub-items, as well as between sub-items.

    • Line: Great for time series.

    • Area:  Illustrates a proportion.

    • Bar:  Provides comparative proportions across dimensions.

    • Scatter or bubble:  Most important when showing comparisons.

    • Pie: Shows relative proportion.

    • Spider:  Utilised when comparing a number of items – usually more than 4 or 5.
       

  4. Think about your colours: Use them to both reflect the content, such as related to branding or national flags; and use to highlight the important content and likewise mute the background information.
     

  5. Understand the nature of the content you want to visualise:  Is it showing comparative proportions across dimensions - then consider bar charts, if it's time series - then line charts will more likely do the job.
     

  6. Highlight the key context of the data data you are showing: For example, if the point is about the significant growth rate of one variable to others then consider how to use colours to allow that to stand out. 
     

  7. Always provide references and data sources along with your visualisations: There is nothing that inspires lack of confidence than a number that doesn’t feel right with no back up.
     

  8. Curate the story:  Remembering that the main job is the communication of the findings and ideas, we should always spend time on the flow and the journey of discovery we want to take the audience on and where we want them to start and to end up.

If you would like to discover more about the value of implementing data visualisation software and strategies within business, contact our team of experts directly.  If you enjoyed this article you can read more of our experts insights on putting data to work online.

Beyond Analysis is a data science, analytics and strategic data solutions and consulting business.

Reach us at info@beyondanalysis.net

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