Source: Christina Wodtke
We all have seen the beautiful infographics, illustrations, and interactive models depicting a complex amount of data. To be able to take raw data, sometimes hundreds or thousands of points, and turn it into a colorful, concise graphic is a skill that is becoming increasingly popular. While it may seem as simple as organizing data and finding a cool way to show it, there is much more thinking that goes on when the designer is creating it.
Essentially, motivation for visualizing the data stems from two questions:
Is the information concept-driven (ideas) or data-driven (numbers and research)?
Is the information exploratory (users can gather their own conclusions from the presented data) or declarative (you are presenting your analysis of the data)?
The possible answers to these questions form the foundation for how to display the information visually.
Concept Driven - Declarative (aka Idea Illustration)
Idea illustration focuses on using simple, metaphoric symbols to show complex ideas, processes, or diagrams. Since people react well to metaphors in visuals (often to find meaning in what they're looking at), these visualizations are good for presentations or teaching, to learn or simplify a process. For example, flowers are often used to depict growth, so people understand that the higher the stem, the more growth has occurred.
The example above addresses the changes in grief with the concept of the valley of despair. The graphic is in the shape of a valley, which allows readers to understand that the stages going downward are negative while the stages going upward are positive. The plateau at the bottom symbolizes where positive and negative converge.
Concept Driven - Exploratory (aka Idea Generation)
Idea generation is less common compared to the other representations out there. Complex and undefined, they are used mostly for brainstorming in team-building activities. It can sometimes use metaphors to convey meaning. Collaboration is a key component, with all ideas staying on the board- no erasing.
Source: She's Shuttered via Flickr
In the example above, there are many diagrams indicating a number of ideas. These ideas are depicted through shapes, pictures, arrows, and charts, and are often created based on what someone else has already put on the board. Once everything is "laid out", it's easier to funnel down to a solution.
Data Driven - Declarative (aka Data Visualization)
Data visualization is the most common and the first thing that comes to mind when thinking about representing information. The analysis is done for the reader to make the point clear and simple.
This example is actually a 6-in-1, with mini graphs to support the title. The design and colors are very simple. Each graph makes logical sense, in that the higher or bigger peaks mean that there was an increase. It's easy to conclude that blue was the previous baseline and pink is the new baseline.
Data Driven - Exploratory (aka Visual Discovery)
These are what I consider the fun ones. There are lots of complex data that does not have an analysis presented. It allows the reader to interact and come up with their own conclusions. These entail multiple data sets or real-time data, making it very overwhelming if not presented properly.
https://pudding.cool/2020/08/campaign-colors/
This example looks at how candidate diversity impacts campaign colors. The data allows you to search for a specific candidate, or filter by party, color, race, or gender. The filters are great because it allows the reader to manipulate the data easily for discovery. It's also a multi-faceted data set, with several variables.
References:
Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations by Scott Berinato
https://informationisbeautiful.net/beautifulnews/1362-up-up-up/
https://pudding.cool/2020/08/campaign-colors/
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