As a society, we are always looking to learn things faster and simpler. Exerting the least amount of energy as possible is a MUST. When presented with a wall of information, I admit that even I cringe and then proceed to Google various sources in order to find somewhere that will tell me exactly what that novel of an article was trying to say in bite-sized chunks. It is arguable that I might have gotten my answers quicker had I just sat there and read through the entire article – but does that mean I would have understood it?

The answer to that question is debatable, but the appetite of the general public is definitely shifting towards easier ways to depict data. Think about a Fitbit. This piece of technology works with your body, phone, and huge online health databases in order to help you lose weight. The Fitbit and the app help you track heart rate, steps taken, calories consumed, and more. With all of this data, it can be hard to comprehend if what you are even doing is positive for your health. Through simplified data visualizations, cues, and corresponding short blurbs of text, users are able to see all of those metrics and understand them easily.

Screenshot of the Fitbit app displaying various forms of data visualization.

But what are data visualizations and how are they changing how we serve information? According to Tableau, data visualization is a graphical representation of information and data. Through visual elements like charts, graphs, and maps, we can provide an accessible way to see and understand trends, outliers, and patterns. How cool is that? So whenever you look at your Fitbit app and see the circles filling up and fireworks happening – that’s your cue that you are taking the right steps because your data is showing you if you’re on track.

Data visualizations provide a strong guide as to what exactly the data is trying to portray. While the graphs and other representations are helpful, people interpret data in a variety of ways so including a key or using a widely understood format assists with retention. Why bother putting in the work to make data simpler if no one can understand it? In this article, Hubspot echoes this sentiment. If one cannot communicate their data through a comprehensible visualization, they might as well throw in the towel. If your data is presented ineffectively, you’re essentially diluting your message and incorrectly communicating information.

“An editorial approach to visualization design requires us to take responsibility to filter out the noise from the signals, identifying the most valuable, most striking or most relevant dimensions of the subject matter in question.”
– Andy Kirk

I’ve always been personally fascinated by how designers can act as visual storytellers. This is largely why I got into graphic design and related creative fields. I loved being able to take something abstract and give it life via iconography and symbols. This week, I put my skills to the test, not as a designer, but instead a data communicator. For this test, I used a data set on World Atlas that outlined which countries drank the most tea circa 2016.

To visualize this data set, I used teabags. This particular tea was in my house in mass quantity and thus was my best choice to use as a data medium. Please note that brand does not have anything to do with the data. For World Atlas’ data set, I equated 1 full teabag with a label as 1 kg. The label served as a way to show decimals. Depending on how much was drunk, the label was cut into small pieces or simply removed from the bag to insinuate an almost full kg of tea. Looking back at my handiwork, I’m noticing that my visualization for Ireland is technically incorrect. Instead of a full label, it should only have a portion of the label to represent the .19, like New Zealand does. Next time, I will pay closer attention to my scale and make sure that it stays consistent during development. Overall, this was a great learning exercise and I think I gained a lot more appreciation for those who consistently produce accurate data visualizations for the masses to enjoy and learn from.

Sources Cited:


Data visualization beginner’s guide: a definition, examples, and learning resources. (n.d.). Retrieved from

Mannon, N. (2019, January 18). Persuasive Storytelling with Data Visualization: Blast Analytics Blog. Retrieved from

Pariona, A. (2016, July 6). Top 10 Tea Loving Countries In The World. Retrieved from



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