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Exploring data visualisation

This article explores data visualisation, and talk in detail about how it plays a vital role in bridging the gap between data and decisions.

As businesses and organisations around us increasingly focus on using data to support their decision-making, data visualisation plays a vital role in bridging the gap between data and decisions.

Being familiar with the principles, tools, and technologies to create visualisations can help organisations analyse the sheer quantity and complexity of information available to them.

What is data visualisation?

Data visualisation is part art, part science. As stated on the Tableau website:

. . . it’s not simply as easy as just dressing up a graph to make it look better or slapping on the “info” part of an infographic. Effective data visualisation is a delicate balancing act between form and function. The plainest graph could be too boring to catch any notice or it could make a powerful point; the most stunning visualisation could utterly fail at conveying the right message or it could speak volumes. The data and the visuals need to work together, and there’s an art to combining great analysis with great storytelling. [1]

What can data visualisation do?

Data visualisation can serve many purposes. These are broadly categorised as exploratory and explanatory.
Exploratory visualisations are a quick way to identify patterns and trends that are worth further investigation. If the raw data is overwhelming, presenting it in a visual form helps you to identify features (including trends or anomalies) quickly.
Month Revenue Cost
January 97883 89452
February 77592 79293
March 85123 75859
Seeing this data in a graphical form made it much easier to see which months the organisation made a profit and which months it didn’t.
Being able to visualise data is an important and effective skill for the ‘communicate’ stage at the end of an analytics project.
At that stage, you know what your data has to say and you’re trying to tell that story to someone else (e.g. your colleagues, external stakeholders, or the general public).
Presenting the results and insights visually helps those who are new to your analysis to understand what’s going on as quickly and easily as possible.
Flow chart shows Import to Tidy to Explore. Within Explore is a cycle containing Transform, Model and visualise. The Explore step then leads to "Communicate" which is highlighted. Adapted from Grolemund and Wickham, p. 3 [2]

Creating explanatory visualisations

When you create explanatory visualisations, you make design decisions that help you to articulate the points you want to communicate and create meaningful stories for your audience. But in the wrong hands, these decisions can obscure information, mislead your audience, or overwhelm them and render your visualisation useless.
As Cole Knaflic puts it:
It can be tempting to want to show your audience everything, as evidence of all of the work you did and the robustness of the analysis. Resist this urge. You are making your audience reopen all of the oysters! Concentrate on the pearls, the information your audience needs to know. [3]

Because explanatory visualisations serve a different purpose from exploratory visualisation:

  • your approach to creating the visualisations will be different
  • your approach to tooling, to an extent, will be different.


1. Data visualisation beginner’s guide: a definition, examples and learning resources [Internet]. Tableau. Available from:

2. Grolemund G, Wickham H. R for data science [Internet]. Available from:

3. Knaflic CN. Storytelling with data: a data visualization guide for business professionals. Wiley; 2015. 288 p.

4. Stevens H. Why outbreaks like coronavirus spread exponentially, and how to ‘flatten the curve’. Washington Post [Internet]. 2020 Mar 14. Available from:

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Data Visualisation: Data Visualisation with Tableau Fundamentals

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