Everywhere you look, there is a story waiting to be told: whether it be from a film, an image, a video game, a book, or…a chart or graph?! Yes, visual storytellers can take data and present it in a way that is accessible for those to see certain trends, outliers, or patterns in such data (Tableau). This method of presenting data is called data visualization, or the visual representation of data based on newly discovered relationships (Watson, 6). Data visualization is becoming increasingly popular due to the excess and instant access to studies, experiments, and information online.
What about not-so-visual data representations like tables and spreadsheets? Believe it or not, according to a study in 2015 by Wakeling et al., there is very low accuracy when trying to interpret tabular data (such as spreadsheets), while basic and familiar charts are much quickly and more accurately interpreted (Watson, 7). Not only does this mean that it is a good idea to turn your data from a bunch of numbers on a spreadsheet to a readable graph, but it is also vital to make that graph simple and straight to the point so anyone looking at it can quickly distinguish differences in graphical properties with little to no cognitive effort (Watson, 5).
It is therefore not only important that your data is presented visually, but how your data is presented visually. If you are looking to make a visualization chart on your own, there are three elements that you must focus on while designing: the spatial substrate, the graphical elements, and graphical properties (IDF). The spatial substrate is the plane that your data will reside, whether that be a 2D or 3D plane. Graphical elements are what they sound like: the points, lines, and surfaces that represent the data and its trends. Graphical properties are the details of each elements, such as its size, orientation, color and shape (IDF).
The type of graph is also something to consider when designing your data visualization. For example, the three most basic, and thus recommended, graphs to use are bar charts (best for categorical data), pie charts (best for part-to-whole comparisons), and line charts (best for time-series relationships) (Data Visualization 101).
As the visual storyteller, all of these concepts and ideas are important to think about because you are “fully responsible for the visualization, not for the data and its accuracy” (DensityDesign, 10). Your goal is to make a story with the data visualization so it can appear interesting to viewers while also being easy to interpret. As if the power to tell a story with data is not good enough, you also have the power to manipulate the story by choosing which data to leave in and which data to exclude, thus adding a persuasive element to your visualization (IDF). Though data influencing happens all of the time, the best practice is to be as honest as possible with your data while creating some compelling and intriguing visuals to go alongside it.
Such visuals are designed by renowned data visualization artist Sarah Illenberger, who takes pictures of real-life objects set up to represent a graph of data. I was inspired by her work to create a visualization of my own, but instead of a data visualization, my image is considered an information visualization because I will be “presenting information about relationships that are already understood” (Watson, 6).
Above is a data visualization chart that I made using real life objects. This is a 2018 study from Statista which asks Americans which devices they play their video games on. Not surprisingly, the majority of Americans are playing video games on their smart devices. This is actually the first year that the majority of Americans are playing games on their smart devices rather than their computers. But as DensityDesign states, we as visual storytellers are supposed to worry about the overall design of the visualization instead of the data, which cannot be changed.
I chose to make a bar chart because not only is the data categorical (Data Visualization 101), but it is the easiest way to make a graph with real-life objects that can be understood for everyone. I had to grab each and every mobile device I own and stack them up in this manner because they are so thin where stacking them all laying down would not have been a very high stack. Once I built this pyramid of smartphones and tablets, I measured it and proportionally calculated the amount of inches per percentage point for each category. This gave me a good idea on how high to make the other three categories so the bars can be proportionate to each other. I searched for all the computers I can find (and when you’re a Mac only family, finding tall computers can be a hassle), game consoles, and handhelds so I can line them up.
In order for the chart to be as simple as possible, I added a white sheet to the background to minimize distractions away from the devices. Upon editing the picture, I blurred the background and changed the sheet’s color to blue so the devices can stand out even more. I also chose to make the entire chart blue, including the background and text, because its best for your viewers to not overuse color nor to make the visualization super fancy, as that may further decrease your viewer’s understanding (Watson, 7). The colors of the devices and consoles being an off-white with black screens and some red accents also contribute to the graph’s simplicity and minimization of colors.
Aside from simplicity and appearance, lining up the devices and framing the image in a way to tell a story is just as important. In order to stay consistent with the devices being used as data points, I omitted other categories from the visualization, such as “other” and “I don’t play games.” This means that the story squarely focuses on comparing the Americans who do play video games, while the percentages are based on all Americans surveyed. There is also a mix of older and newer devices in each category, which serves two purposes: to make the correct height values for each individual bar when setting them up, and to show that any device, regardless of age, still counts in these categories.
After creating this image, I now understand the difficulty in creating a data visualization piece with real-life objects that show its simplicity and tells a compelling story. Having all of the devices stacked up on top of and against each other is a small representation of the amount of devices each American has in their home nowadays, which says a lot about how not only gaming has evolved in terms of what devices are most popular, but how communication has evolved. Data visualization, and in my case information visualization, represents “data in a way that is easy to understand and manipulate” (IDF), and using real-life objects that associate with the data you are visualizing can help make the data much easier to interpret. No matter which type of visualization you make, using all three elements properly will make for an effective data visualization chart and will tell the story that you want it to tell.
“Data Visualization 101: How to Design Charts and Graphs.” Hubspot, Visage. https://cdn2.hubspot.net/hub/53/file-863940581-pdf/Data_Visualization_101_How_to_Design_Charts_and_Graphs.pdf
“Data visualization beginner’s guide: a definition, examples, and learning resources.” Tableau. https://www.tableau.com/learn/articles/data-visualization
Eckstein, Linda. “Sarah Illenberger’s Infographics.” Blogspot, 5 Oct, 2010. http://allmyeyes.blogspot.com/2010/10/sarah-illenbergers-infographics.html
“Information Visualization – A Brief Introduction.” Interaction Design Foundation. Jul, 2020. https://www.interaction-design.org/literature/article/information-visualization-a-brief-introduction
Losowsky, Andrew. Visual Storytelling – Density Design. Gestalten, 2011.
“United States: Which of the following devices do you use to play games?” Statista, 2018. https://www.statista.com/statistics/561386/us-types-of-devices-used-to-play-games/
“Visual Mapping – The Elements of Information Visualization.” Interaction Design Foundation. Jul, 2020. https://www.interaction-design.org/literature/article/visual-mapping-the-elements-of-information-visualization
Watson, Hugh J. Data Visualization, Data Interpreters, and Storytelling. Business Intelligence Journal, 2017. https://d1wqtxts1xzle7.cloudfront.net/52905528/Watson__Data_Visualization.pdf?1493647286=&response-content-disposition=inline%3B+filename%3DData_Visualization_Data_Interpreters_and.pdf&Expires=1600029845&Signature=SsBhk8mxLQ6IUAe4xQRovAfJs2mTWL4qySar3aFWoet5BOTWYe3TQY9~8Vt~6mKNqBMR8tSD0jgLzBfy3wVNobrQOonaeEaKqu43I1ssMdDRs-1noQw2TGRcSFIdp3GtUTZ7FgaP1b4-whVGrxx9usvLtZ3gKCCaRM1ar4pHRHCjP~Co2hrszxBhK~UvsEZ~YxIQbZY-H-d~Ny-D2kRKCLWd4kWUm-5SVsh2e7cK-xWl~4h83wKfZJzpWfsuluxU5HBt1JK7E43u~6HV86av-dvAz9UUENwXWsIitFnAUkLecCP5a8OHPE2rQmlpz4htCrI2QjTtk6Wv768NCiQo-g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA