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How to Visualise Data Effectively:
Tried and Tested Tips for Data Visualisation

This article explores the what, the why, and the how of data visualisation and gives insights in order to help curate an effective and insightful story by using different visualisation techniques.

What is data visualisation?

Data visualisation is a process of turning raw categorical or numerical data into any form of graph, chart, infographic or other format, which presents the data in a more accessible way to enable understanding. It is a powerful way to explain and gain insight from the information, as well as create more value from it by depicting patterns and trends and conversely outliers. But with that also comes the territory of creating such content that is effective to the reporting user.

Why is data visualisation important?

Data visualisation is an important way for any business to create more value from many complex numbers that otherwise would be just thousands of excel spreadsheets. Not only that, but a great data visual with great storytelling stimulates our brains to produce certain hormones that increase generosity, compassion, and trust.

An example of this can be shown by examining Anscombe’s quartet, which comprises four data sets. From looking at the table, we could make an assumption that the numbers are all relatively similar; however, once the data is visualised on a scatter plot, it presents a different idea.

Anscombe's quartet graphs.png
Anscombe's quartet table.png

The Anscombe’s quartet example clearly shows that it is important to visualise data, to not only confirm or refute assumptions, but also for ideal results.

Examples of visualisation tools

Data visualisation tools are cloud-based applications that help to represent data in easy graphical formats.  The following programs are some of our favourites, used by our data visualisation experts, to create bar charts, pie charts, column charts, and more for our clients. 

  • Tableau - One of the most used and recognised data visualisation tools available. It is well known for being a powerful and fast tool with a great deal of available features such as interactivity. While Tableau is a premium tool, there is a free version of Tableau public, which is the same tool with almost all of the features except with the limitation of saving your work locally.
     

  • Power BI - A Business Intelligence and Data Visualisation tool, which allows you to convert data from different data sources into interactive dashboards and reports. It gives numerous software connectors and services.
     

  • Figma - While figma is not primarily a data visualisation tool its unlimited features and constant updates allow for very eye pleasing visualisations. It does not have built-in data source features that can turn excel spreadsheets into charts, but with plugins like Chart, Charts or UI Chart kits such features are available.

How to visualise data effectively

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

1. Know your audience - When visualising data, it is important to consider who the audience is and what they already know. In instances i.e. when presenting for top level executives, we keep in mind that they might not be familiar with interactive dashboards or any intricacies, so it is important to keep the report straight to the point and as simple as possible.
 

2. Define the objective of your visualisation - Data visualisation objectives can vary depending on the purpose of the report. Overall, the main goal of data visualisation is to inform and explain complex information to the end user. Consequently, it is important to define these objectives before beginning a visualisation. Some of the objectives might be; to predict; to tell; to identify; to discover; to find patterns; or to inform.
 

3. Select the right type of chart - Depending on what kind of data set we are visualising, there are many types of charts that can differentiate according to the goal of the report:

  • Column chart – A simple way to show two numerical comparisons between categories, best used for small and medium sized data sets.

  • Bar chart – Used when working with categorical data that cannot be put on a continuous scale. For example, a bar chart might be used to document all the favourite types of drinks of a businesses’ colleagues.

  • Graphs – A very straightforward way to compare various categories of data sets. Also, commonly used in marketing to show comparisons between numerical values, i.e. survey results.

  • Pie chart – Also very commonly used type of visualisation to compare data, such as budget allocation or market segmentation.

  • Table – Although not ideal for large amounts of data, tables help to display both data points as well as graphics such lines, graphs, and etc.

  • For Big data Visualisation - Types are separated into categories such as temporal, multidimensional, hierarchical, 3D or Linear.

4. Consider the colour scheme - Colours in general are an important factor to any visual content. When creating a visual data report, the colour scheme allows us to tell a story and helps with both setting the mood and secondly guides the end user and draws attention to particular features. For example, this chart from NYU Twitter account:

NYU Graphs.jpg

Overall, looking at this chart the chosen colour scheme makes it uneasy to try to differentiate categories, the subheadings are also too bright. As noted by Kaiser Fung, this can easily be fixed with using two colours with different shadings: 

NYU Graphs 2

5. Consider the nature of the content - When visualising the data, we consider what type of content we want the end user to gather, whether it’s a numerical comparison between categories on column chart or segmentation on pie chart.
 

6. Highlight the key context of visualisation - It is key to guide the reader through the report by using the right colour scheme and highlighting parts that are significant i.e. certain growth rate.
 

7. Provide reference and data sources - A visualised data set without any references is just a graphic that doesn’t feel right, therefore providing sources ensures the end users trust.
 

8. Curate the story - Data visualisation is not so much about the visual aspect, as it is about guiding the listener or reader through a story. It is about providing context to any visuals and connecting a series of happenings. Neural coupling explains how a great story activates certain parts in the brain that allows the listener to turn the story into their own ideas and experiences.

If you are interested in how we implement data visualisation techniques for our clients, see our data visualisation and reporting page or contact our team. For more strategic data insights from our data strategy consulting experts check out our insights page.