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See your data before your very eyes

Topic: Visualisation
by Matthew, 2020 Cohort

In media and academia, authors use data-sets and quantitative information to exemplify a point. This is where data visualisation comes into the fold. Due to the complex nature of large data-sets, authors use graphs and other visualisations to simplify and demonstrate key trends. This representation can be used as a separate entity but is typically used in conjunction with a paper to comprehensively convey a point. This visual data representation includes data maps, time-series, relational graphics, pie charts and Venn diagrams, among others.

So why do people like to use data visualisation? 65% of the global population are visual learners. Authors want to be able to represent complex and multiple data sets in an extremely clear and easy to understand way. This way, the point of their data-set is more easily conveyed, and in turn the entire paper is more effective.

A well-executed data visualisation is significant in aiding the effectiveness of your paper. So, what constitutes a good data visualisation? It is difficult and slightly ironic to describe exclusively in text what makes a good visualisation, without the proviso of using visual examples. However, there is basic criteria. Visualisations need to be simple enough to be able to convey relevant data easily, and to be accessible. They need to be accurate in the representation, to simplify complex concepts, to be understandable at a glance. However, what is considered “good” is subject to opinion.

Whilst there are plenty of examples of well-executed visualisation, there are even more examples of poor data visualisation. There are plenty of ways to create ineffective and misleading representations of data. A popular use in media is the distortion of the Y-axis in a conventional graph to exaggerate differences in data. Other ways include having obvious bias within the representation in order to suit an alternative narrative and poor quality or small data-sets. An example is landmass distortion closer to the poles in a Mercator Projection map (pictured). Visualisations can be considered ineffective if either the author or audience have a lack of knowledge regarding the data. Similar with good visualisations, there is a certain degree of subjectivity and ownership of opinion in data visualisations.

The objective of this entry is to inform and make you think about data visualisations you have previously experienced, good or bad. As a consumer of both media and academia, we need to be knowledgeable and vigilant about what data visualisations we are believing, and what visualisations we are discarding of. Data representations are prolific and effective tools of conveying information, and it is our job as consumers to understand the biases and techniques authors use to exploit information to create alternative narratives.

Takeaways:#

• Data visualisation as a representation of a set of information or data-set
• Used within media and academia to present complex data in a more succinct manner
• Good visualisations are accurate, have large data sets and convey with simplicity
• Bad Visualisations distort data, use small data-sets, and have bias
• We should be knowledgeable about data visualisation to make informed opinions about questionable presentations.

Explore this topic further#

• Edward Tufte on The Visual Display of Quantitative Information{.link-ext target=”_blank”}
• Harbisson- I listen to Colour{.link-ext

Return to Visualisation in the Primer

Disclaimer#

This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au

In media and academia, authors use data-sets and quantitative information to exemplify a point. This is where data visualisation comes into the fold. Due to the complex nature of large data-sets, authors use graphs and other visualisations to simplify and demonstrate key trends. This representation can be used as a separate entity but is typically used in conjunction with a paper to comprehensively convey a point. This visual data representation includes data maps, time-series, relational graphics, pie charts and Venn diagrams, among others.

So why do people like to use data visualisation? 65% of the global population are visual learners. Authors want to be able to represent complex and multiple data sets in an extremely clear and easy to understand way. This way, the point of their data-set is more easily conveyed, and in turn the entire paper is more effective.

A well-executed data visualisation is significant in aiding the effectiveness of your paper. So, what constitutes a good data visualisation? It is difficult and slightly ironic to describe exclusively in text what makes a good visualisation, without the proviso of using visual examples. However, there is basic criteria. Visualisations need to be simple enough to be able to convey relevant data easily, and to be accessible. They need to be accurate in the representation, to simplify complex concepts, to be understandable at a glance. However, what is considered “good” is subject to opinion.

Whilst there are plenty of examples of well-executed visualisation, there are even more examples of poor data visualisation. There are plenty of ways to create ineffective and misleading representations of data. A popular use in media is the distortion of the Y-axis in a conventional graph to exaggerate differences in data. Other ways include having obvious bias within the representation in order to suit an alternative narrative and poor quality or small data-sets. An example is landmass distortion closer to the poles in a Mercator Projection map (pictured). Visualisations can be considered ineffective if either the author or audience have a lack of knowledge regarding the data. Similar with good visualisations, there is a certain degree of subjectivity and ownership of opinion in data visualisations.

The objective of this entry is to inform and make you think about data visualisations you have previously experienced, good or bad. As a consumer of both media and academia, we need to be knowledgeable and vigilant about what data visualisations we are believing, and what visualisations we are discarding of. Data representations are prolific and effective tools of conveying information, and it is our job as consumers to understand the biases and techniques authors use to exploit information to create alternative narratives.

Takeaways:#

• Data visualisation as a representation of a set of information or data-set
• Used within media and academia to present complex data in a more succinct manner
• Good visualisations are accurate, have large data sets and convey with simplicity
• Bad Visualisations distort data, use small data-sets, and have bias
• We should be knowledgeable about data visualisation to make informed opinions about questionable presentations.