When to trust data visualisation (and when not to)

If you ask whether people prefer to see images rather than text to process an information, I’m pretty sure the answer would be a resounding yes. Why?

Because humans are visual creatures.

Research from 3M corporation has found that we process images 60,000 times faster than text. This might explain why we find visual data is more appealing and attractive:

simply because we can understand it quicker.

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Image 1

This might also explain the increasing number of data journalism we see everywhere we go, whether it’s on TV, social media, and even newspapers. The emergence of data journalism certainly, has not been ignored by journalists or even amateur bloggers.

A staggering number of people and businesses are racing and competing against each other to make the best and most creative infographics that are appealing to the audience.

However, often, at the expense of credibility and accuracy.

As discussed in the previous blog post, there are some problems associated with infographics and data journalism. Fisher’s ‘map of the world’s most and least racially tolerant countries‘ can perhaps serve as a perfect example of how data journalism are often flawed and misleading, yet, it is blindly accepted and believed by millions of people in a heartbeat.

The fact that colours and designs have more impact on people’s perception of messages, rather than the actual credibility of the data source, says a lot about the issue of interpreting infographics.

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Image 2

So how can this issue be solved?

First of all, it is important for anyone that create infographics or data visualisation to disclose where the sources associated with their data and graphic are coming from, and more importantly, how their data/work should or should not be treated as scientific fact.

And secondly, by raising public awareness about the issue of accuracy in data visualisation, to prevent the spread of fake news or misinformation.

But how do people identify inaccurate/faulty data?

John Burns Murdoch came up with this list you have to check before believing in any data visualisation. It is not anything revolutionary, it is just the kind of thing that people can do mentally and automatically in their mind when seeing a data. If the data failed to check all the lists provided, then it is probably best to not trust the data.