Skip main navigation

Data Visualization Techniques

It’s widely accepted that data in its original form is of little value. In the words of Richard Saul Wurman: Raw data has no inherent value. It must be imbued with form and applied to become meaningful information. Yet, in our information-hungry era, it is often allowed to masquerade as information.

It’s widely accepted that data in its original form is of little value. In the words of Richard Saul Wurman:

Raw data has no inherent value. It must be imbued with form and applied to become meaningful information. Yet, in our information-hungry era, it is often allowed to masquerade as information. [2]

Data Visualisation Process

Data visualisation starts well before you create a graphic or image. The process starts with the raw data – the records that are held in systems and databases.

Data analysts interpret the data and present and organise it into information. This process of interpretation is susceptible to bias and subjective perception. Different analysts might draw different conclusions from the same numbers. This can result in misrepresenting the data or visualising misleading findings.

Analysts can’t be completely unbiased in visualising data, but they should always choose the most ethical way to perform and present our research. The first step towards ethical visualisation involves recognising where or when we might unintentionally mislead our audience or misrepresent the data.

Data Visualisation – a Human-Centred Approach

To create compelling data visualisations, we have to know who the visualisation is for and how they will interact with it. A visualisation is of little value if the end-user or target audience does not understand it or it does not meet their needs.

Consider these questions:

  • What is the audience’s current understanding?
  • How much detail do they need?
  • What actions or decisions does the visualisation inform?
  • How much time do they have to see the visualisation?
  • Will the visualisation stand alone or be placed in context?

Consider this simple example, which is based on findings from the 2021 Youth Index. [3]

Case 1

Graphic shows a vertical bar chart on "Generation stressed: how often do you feel anxious?" The following are the data: Always 18.7%; Often 37.3%; Sometimes 29.4%; Never 14.6%. Source: Youth Index 2021 Report.

The audience for this visualisation is young people. The amount of information, and the tone, is designed for a young audience. A young person who encounters this might gain a better understanding of the issues; for example, they might reach out for help, or they might use what they learn to help a friend.

Case 2

The same data can also be presented as in this following visualisation.

Graphic shows a chart on "Anxiety is on the rise. Levels of anxiety in young people in the UK are on the rise. The chart shows the percentage of youth reporting they are “always” or “often” feeling anxious. Data was weighted and is representative of 16 to 25-year-olds." Y-axis reads from bottom to top: 20, 40, 60. X-axis (Year) reads from left to right: 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021. There are dots plotted linearly on the area of the Y-axis: 20 - 40. There's an upward trend line starting at (2009, 10) and ends (2021, 60). Source: Youth Index 2021 Report.

Here, the audience for this visualisation is parents, teachers, and policymakers. The person encountering this information might learn something new, organise resources to support young people in their school or community, or implement national programs or campaigns to tackle the issues highlighted by the visualisation.

Both visualisation examples are practical for their audience, but neither would be effective if it was presented to the other audience. In summary, it is critical to choose the proper data visualisation for the right audience. For that, data analysts must know the different types of charts.

References

  1. Wurman R. S. Information Anxiety: Towards Understanding. [Article] Scenario Journal; 2012. Available from: https://scenariojournal.com/article/richard-wurman/
  2. The Prince’s Trust Tesco Youth Index [PDF]. Prince’s Trust; 2021. Available from: https://www.princes-trust.org.uk/Document_Tesco-Youth-Index-2021.pdf
This article is from the free online

Introduction to Data Analytics with Python

Created by
FutureLearn - Learning For Life

Our purpose is to transform access to education.

We offer a diverse selection of courses from leading universities and cultural institutions from around the world. These are delivered one step at a time, and are accessible on mobile, tablet and desktop, so you can fit learning around your life.

We believe learning should be an enjoyable, social experience, so our courses offer the opportunity to discuss what you’re learning with others as you go, helping you make fresh discoveries and form new ideas.
You can unlock new opportunities with unlimited access to hundreds of online short courses for a year by subscribing to our Unlimited package. Build your knowledge with top universities and organisations.

Learn more about how FutureLearn is transforming access to education