Why 2020 was the year of data viz

The biggest design trend from last year made visualizing data a mainstream thing.

Rubens Cantuni
UX Collective
Published in
5 min readMar 11, 2021

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illustration represting data visualization
Data viz vector created by vectorjuice www.freepik.com

If I had to point my finger at the biggest trend in design in 2020 I’d definitely point at data visualization.

Sure, it’s nothing new. Data viz crawled from its origins on scientific papers into mass adoption earlier than that, especially during big events such as US elections, thanks also to the evolution of technologies that allowed us to create interactive visualizations. But 2020 has been arguably the year when everyone became obsessed with it.

What is data visualization?

In short (very short), data visualization is the representation of data (usually numerical) with graphics.

It’s a way to make it easy (or easier) to understand lots of information at a glance; a visual aid to digest hundreds, thousands, or millions of entries in seconds, understand trends, and help draw conclusions.

Charles Joseph Minard’s 1869 diagram of Napoleonic France’s invasion of Russia, an early example of an information graphic
Charles Joseph Minard’s 1869 diagram of Napoleonic France’s invasion of Russia, an early example of an information graphic — source: Wikipedia

We could write books about data viz, but this is not that kind of article, so allow me to jump forward to 2020.

Johns Hopkins’ COVID-19 dashboard

When COVID-19 began its way to global domination, a dashboard made by Johns Hopkins University became viral.

Johns Hopkins University COVID-19 dashboard

I remember looking at this with my colleagues (when we still were in an office) when the global cases jumped from 4 thousand to 7 thousand in a day (and that’s the moment we started to worry a little bit. Little did we know…).

Not a fancy visual design, but it does the job pretty well. The information is easy to understand, you can get an idea at a glance, but also, if you want, dig deeper into numbers.

Most news outlets started to link or refer to this in the early articles about COVID-19 spread around the world. In the meantime, they were all working on their own version for the pandemic visualization.

screenshot of NY Times covid tracker
NY Times Covid tracker

Everybody started to look at the pandemic unfolding through one of these data viz trackers. News websites provided daily updates using charts and diagrams to better explain how the situation was evolving and people didn’t just get used to it, but for many, it almost became an obsession.

Data visualization became an important tool to understand the situation, how this was unfolding, and how at-risk we were in the place we lived. Rather than reading a full article just stating numbers, it was easier, faster, and more comprehensible to look at a chart or a map. With easy interactions, we could also adjust the set of data to visualize the piece of information we were interested in (things like location, age group, gender, etc).

COVID-19 cases by country from Il Sole 24 ore, Italian newspaper
COVID-19 cases by country from Il Sole 24 ore, Italian newspaper
KEA visualization to track support measures for the cultural and creative sector across Europe
KEA visualization tracking support measures for the cultural and creative sector across Europe

US Presidential election

Another big moment for data viz in 2020 was the Presidential election in the USA. Probably never before in history (not even recently) people got so obsessed with tracking data like on this occasion.
The interest was huge, globally, the outcome has been unpredictable until the very last moment, states were swinging, then they were not, then they were again… And people got hooked. Everybody was clicking and refreshing the charts on the NY Times, CNN, Google, and basically any news outlet out there.
People took screenshots, shared them on Twitter, retweeted, commented, and the usual craziness when something goes viral.

CNN Presidential election data viz
CNN Presidential election data viz

So, in 2020 we had two major global events(I guess we can say the US election is a global event) where data visualization played a huge role in how people were able to keep up. We can say that data visualizations went viral.

With great power comes etc…

Data visualization can be easily used with malign intents to skew public perception of some facts. By slyly use different scales, for example, or by using the wrong kind of representation for the set of data, or comparing logarithmic with linear data, someone could easily create misleading visualizations.

misleading chart about COVID cases in Florida
confusing chart about COVID cases in Florida — Source

The following image went viral on Twitter right after the election. It shows pretty well how data viz can be misleading. The map on top shows the winner in each county, and there’s an overwhelming presence of red (Republicans), but the map on the bottom is adjusted for the population in each county, and here the blue (Democrats) is clearly winning.

map comparing vote by territory and vote by population
if you know the original source please let me know

Data visualization is a powerful communication tool that has to be handled with great care. Now that or sort of charts are used in political campaigns all over the world, we really need to pay attention at how this data is visualized.

As designers, our duty to make sure data viz is used for informing people honestly and fairly. Wrong use of data viz can be one of the most dangerous dark patterns out there.

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The UX Collective donates US$1 for each article published on our platform. This story contributed to Bay Area Black Designers: a professional development community for Black people who are digital designers and researchers in the San Francisco Bay Area. By joining together in community, members share inspiration, connection, peer mentorship, professional development, resources, feedback, support, and resilience. Silence against systemic racism is not an option. Build the design community you believe in.

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