Data visualization: Is a picture worth a thousand words or a wasteful effort?

“A picture is worth a thousand words.”

That’s why ‘Data Visualization‘ was born.

Actually, I’m a big fan of data visualization. Without this beautiful infographic, I will never understand how a car engine work(lol).

how-care-engine-works infographic

However, the way we are praising data visualization is somewhat pompous. It makes us completely blind to the risks of visualized data.

Data misinterpretation

In a study about graphic representations of information, Bresciani and Eppler conclude that there are three potential risks inherent in visualization:

De-focused: When creating a graphic, it can be tempting to focus more on the layout than on function. Be careful! Too many unnecessary ornaments or too many unrelated elements emphasized at the same time can distract audiences. They don’t know where to to focus, thus get completely confused about the graphic.

Disturbing: Some images can shock or upset the viewers. This echoes with the findings from Seeing Data project, a research conducted by data visualization expert Andy Kirk. Andy indicates that although the viewers tend to not exactly remember the data from the graphics, they could remember the overall impressions, and, significantly, the emotions that the graphics evoked. Therefore, designers need to pay careful attention to the emotional aspect of graphics because this can affect the way the audiences interpret data through visualizations.

Cultural and cross-cultural differences. Because of the heterogeneity of audiences, some graphic representations may be misinterpreted. For example, Western viewers tend to focus on the foreground, while east-Asian audiences focus on the whole picture and the background. Color meaning also varies in different cultures. Thus, designers need to consider cultural elements, especially when creating visualizations for cross-cultural audiences.

The rise of lazy audiences

We need to do infographics because our audiences become so lazy. Reading data is more work than their lazy brands want to do. This makes me upset thinking of infographic and all kinds of data visualizations.

Data journalist’s role is to access and present the data on the public’s behalf. However, it is public’s responsibility to analyze and draw understanding from data themselves.

If someone is interested in a specific topic, he/she will give the most effort to consume a whole load of information. If they think this kind of knowledge is too boring and useless for them, then creating an infographic to help them understand that topic is just a waste of time. What for?

We tend to think data visualization is trendy. But why we need to make that fancy animated infographic just to explain how to eat an artichoke (lol).


Again, I love data visualization. However, data visualization sometimes can make audiences confused about the content rather than making it easier for them to consume the data. Moreover, as media professionals, we need to consider carefully when data visualization should be used and how. It’s a waste of time and effort to follow the graphic trend without a clear purpose.





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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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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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.

Confirmation bias in data-journalism

Data journalism can simply be explained as the use of data as a tool to tell or explain a news story. It can be in a form of infographics, statistics, charts, graphs, etc.

So who are these data journalists and how to be one?


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The good news is that you don’t necessarily need to be part of a large corporate media, a developer or even a coder to be a data journalist. Although of course, working under large corporations like The Times has its own benefits in terms of having more budget and resources and having people with actual reporting experience and skill.

Even so, technically, anyone can be a data journalist. All you need is a web access and you’re settled.


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With websites like OECD Statistics, World Health Organization and UNData giving free public access to everyone, anyone can do it.

Now although everyone can do it, it is important to note that not everyone can do it well.

However, its major advantage could also be its major disadvantage. The fact that anyone can be a data-journalist could also pose as a potential threat for the society.

It is important to highlight that there is a false sense of impartiality with data journalism.  For example, have you ever purposely put in a lot of those complicated statistical, numerical data in your presentation just to make it looks more professional and credible? I know I have.

Now I’m not saying it’s wrong, we do it because many people (I’m guilty for this too) will actually fall for that trick.

They might assume that by reading all these numbers and seeing all these graphs, it must be true. Yes, the information presented might be true, but it doesn’t necessarily mean it’s not biased. As mentioned by Sarah Cohen from The New York Times, just because it’s data doesn’t mean it’s not subjective.

Like any other types of journalism, there is always a possibility of author’s bias. So instead of critically analysing the facts, consider their good and bad aspects, conclude it based on their pros and cons, and present it in a way that is unbiased, many authors engage in a confirmation bias. This happens where authors tend to search, interpret and collect the data in a way that confirms their prior beliefs.


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In short, it exists when the author wants what they believe to be true, which might lead to tainted or misleading results.

An example can be seen from one of Buzzfeed article, where they claimed that Democrats watch more porn than Republicans.

Guess where the source was from? Yup, PornHub.

So although it is a data-backed journalism, it is still an opinion journalism. So as we wade into the ocean of data journalism nowadays, let’s not forget that it is also important to be aware of what we can and cannot trust.