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As McCandless posits, data visualization creates a landscape that guides your eyes through the data. Visual representations of data allow the viewer to uncover trends and patterns within the data that might otherwise be difficult to see given just the numbers alone. Common examples of data visualizations include line charts, bar charts, pie charts, histograms, scatter plots and maps (Stobierski). Each of these graphs allows for easy interpretation of data sets. As IBM describes it on the Analytics page of its website, data visualization allows you to “find the story hidden in your data.” Yet there is a difference between simply visualizing data and presenting data. Imagine coming across this scatter plot (click here to view).
Some components of this graph are relatively simple to interpret. The x-axis represents world press freedom score. The y-axis represents world happiness report score. The points on the plot seem to represent different countries. These inferences are easily made by anyone who can read basic information contained within the graph. Yet there are questions that arise when viewing this graph for the first time. The viewer might wonder why the plot points vary in size; do these sizes represent the total populations of each country? Or do the sizes vary based on some other parameter? The viewer might wonder what a world press freedom score is, and how this score is determined for a given country. Similarly, the viewer might ask how world happiness report scores are collected.
There are plenty of questions about the data that are not addressed in this visualization. Clearly, there is something missing.
Every visual representation of data needs supplementary information in order to communicate the full story to the viewer (Nussbaumer Knaflic). The means by which this supplementary information is conveyed can vary greatly. In the case above, one might include relevant information in the form of a blurb or a caption underneath the graph. Initially, the viewer’s eyes are drawn to the colorful graph at the top of the screen or page. Then, upon further inspection, the supplementary information is presented at the bottom of the graph.
Captions have traditionally been used in this way; they serve to give the viewer the necessary context in order to understand the data. Yet there are plenty of other ways that context can be presented. In McCandless’ TED-Talk (see Home), there is a series of visual representations of data embedded throughout the presentation. What is noticeably absent throughout the talk is written context for these graphs. This is because McCandless uses his words in order to walk the viewer through his data visuals. In the case of McCandless’ talk, it was his verbal cues that served as the context for the data.
But what if you are not giving a talk? What are other ways to give sufficient context to your data without using captions or verbal cues?
Infographics
Infographics
Without question, one of the most popular ways of providing context for data in the digital age is via infographics. Infographics, in a general sense, are representations of information that can include both visual and textual elements. They can be found anywhere from news outlets to restrooms. Even the earliest cave paintings can be classified as infographics by today’s standards. In the simplest of terms, infographics are meant to convey information to the viewer.
But as mentioned above, the scope of infographics is not limited to just pictures and text. Infographics in the digital age often include data, and can even include data visualizations. Take this infographic from the CDC as an example:
Here we can see a number of textual elements within this infographic. The heading of this infographic (“Not Everyone with COVID-19 Feels Sick”), the caption on the left-hand side (beginning with “New report…”), as well as the bottom blurb (beginning with “Practice social distancing…”) are all textual components of the infographic. However, looking at the right-hand side demonstrates an example of data visualization embedded within the overall infographic. You can see the data (1 out of 5 reported no symptoms) being represented by five silhouettes, with one of them highlighted in yellow. This piece of data visualization, although an extremely simple pictorial graph, demonstrates the effectiveness of using infographics as a means of providing context for data visualizations. Furthermore, infographics can provide a visually pleasing way of navigating both the data and the context. Imagine if this set of data from the CDC were presented in a different way. Imagine if the pictorial graph had been presented at the top of the graphic, with the supplementary information being presented as a caption at the bottom of the graph. Wouldn’t this have felt a bit dull? Would you have received a different message if the data had been presented in this way? Would you even remember the data at all after viewing it in such a traditional fashion?
Infographics have the potential to not only incorporate data, but to make the data something to remember (Torban). Yet this is not to say that simply slapping your data into an infographic will sufficiently communicate your message to the viewer. Designing data-driven infographics is an artistic endeavor. Like all pieces of art, data-driven infographics require proper planning and execution. Moreover, they require the creator to have a specific approach prior to carrying out the design.
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