When visualizing information, selecting a decent chart or graph type to display your data may seem like a no-brainer. We often throw things together quickly, choosing a chart type on impulse. It might surprise you that this is not the ideal approach to visualizing information. In fact, a better practice involves asking yourself a couple of questions about your data.
The Nature and Purpose Questions
According to Scott Berinato, you should consider the following questions about the nature and purpose of your visualization in order to start thinking visually:
- Is the information conceptual or data-driven?
- Am I declaring something or exploring something?
These two questions combine in a 2×2, creating four possible types of visualizations: Conceptual-Declarative, Conceptual-Exploratory, Data-Driven-Exploratory, and Data-Driven-Declarative.
The Four Types
Conceptual-Declarative
In the quadrant between conceptual and declarative lies Idea Illustration. The goal of idea illustration is to simplify complex concepts. This kind of visualization illustrates processes and frameworks, commonly resulting in a simple and metaphorical visualization. Idea illustration is often utilized in presentations and teaching and can often involve trees, bridges, and circles, showing up as decision trees, org charts, tree diagrams, cycle diagrams, and the like.
Water cycle diagrams are a good example of idea illustration because most of them highlight the main stages (evaporation, condensation, precipitation, and collection) in a cycle format. Most of these diagrams do not visualize the complexities of the water cycle. They often omit additional phases that may make the concept more challenging to understand, including surface run-off, percolation, transpiration, and more. Instead of throwing all aspects of the cycle into one diagram, they are grouped into four basic categories. Through teaching, these categories are further explained. The simplification that exists within water cycle diagrams is a key feature of conceptual-declarative visualizations.
Conceptual-Exploratory
Also known as Idea Generation, conceptual-exploratory visualization is “typically used in group brainstorming sessions to gather as many diverse points of view as possible.” Idea generation is used for discovery, learning, concept simplification, collaboration, and design thinking. This form of visualization often takes place informally, involving whiteboards, sketchbooks, butcher paper, napkins, etc. This approach is a “go-to for thinking through complexity.”
Design thinking involves a lot of idea generation, including mind mapping. Mindmapping is a graphical technique in which participants build a web of relationships. By branching out from a central main idea, this strategy creates a visual representation of related terms and ideas. Mindmaps are successful conceptual-exploratory visualizations because they support the discovery and dissection of complex ideas.
Data-Driven-Exploratory
The third visualization type exists between data-driven and exploratory. Visual Discovery seeks to identify trends, make sense, and achieve deep analysis. Within the 2×2, this quadrant is the most complicated, as it incorporates big, complex, and dynamic data and covers two subcategories: confirmation and exploration. Exploration is open-ended, while confirmation is more focused exploration.
The $Billion Dollar o-Gram, explained further by its creator, David McCandless, is an example of a data-driven exploratory visualization. It displays American spending, earning, potential giving, fighting, giving, owing, and accumulating. McCandless describes this visualization of complex data as a “landscape that you can explore with your eyes, a kind of map, a sort of information map.” Over the years, there have been many iterations of the $Billion Dollar o-Gram, including interactive versions, that allow users to explore and make sense of the vague “billions” of dollars often mentioned by American news outlets. This evolving data promotes discovery and analysis, characteristics of data-driven exploratory visualizations.
Data-Driven-Declarative
Finally, data-driven-declarative visualizations are also called Everyday Data Viz. This form of visualization is the one most people choose on impulse. Everyday data viz looks like conventional charts and typical graphs including, bar charts, pie charts, line charts, and scatter plots. It is often utilized in formal presentations and serves the purpose of providing context and affirming information. The data behind these charts is often simple and of low volume. Everyday charts and graphs support a simple narrative and make clear and distinct points based on established data.
Much like numerous pie charts we observe and create regularly, Lumina’s opportunity pie chart clearly depicts how people in the United States understand opportunity based on their 2019 National Racial Justice and Equity Framing Survey. This chart makes it clear that the majority, 56% of the participants, agree that “opportunity isn’t equal in the United States. This is followed by 35% who agree that “everyone has equal opportunity in America.” Finally, the remaining 9% agree with neither of the preceding statements. The data behind this chart is simple, separated into three categories. The chart itself is also simple, straightforward, and declarative, making clear and distinct points.
Conclusion
We often default to everyday data viz when seeking a way to visualize important information. However, it is imperative to identify the visualization that suits the specific data you are working with. The four types 2×2 should serve as a guide, informing the decisions you make about the visualization of your information. These decisions will determine “the skills you’ll call on, the tools you’ll use, and the media you’ll visualize.” Furthermore, asking the necessary questions will ensure that your visualization is successful and effective.
Sola's Thoughts 