CHOOSE YOUR CATEGORY

Information design can be divided into two main categories, exploratory and explanatory (aka declarative), shown as the bottom and top points on the vertical axis in the above chart (Marchese, n.d.). These categories can be broken down further by delineating between content that is conceptual or data-driven, as shown on the horizontal axis.

Scott Berinato, senior editor at the Harvard Business Review and author of the book Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations describes how choosing the right category can be helpful in planning and creating good visualizations.

Appropriately, he provides a visual to foster our understanding. Starting in the upper left-hand corner and moving counterclockwise, the four quadrants are:

  • Conceptual & Declarative (Idea illustration)
  • Conceptual & Exploratory (Idea generation)
  • Data-driven & Exploratory (Visual discovery)
  • Data-driven & Declarative (Everyday dataviz)

Let’s briefly discuss each of the categories and look at examples of each.


(Berinato, 2016)

Need to explain a process or a concept? Want to show how your organization is structured? Need to show a process flow? Illustrating ideas is the domain of the Conceptual & Declarative category of visualization (Berinato’s 2 x 2 grid is in itself an example).

The flowchart below is one I created to explain the training approval process to my colleagues at work.

NJ Department of the Treasury Training Approval Flowchart

In my Typical Approval Process chart the concept I am sharing is the approval process. My declaration provides an answer a question I hear all too often, which is “What do I need to do to get the training I need?”

Note that both examples provide simple images that help facilitate the user’s understanding of a particular concept or process.


(Berinato, 2016)

Conceptual & Exploratory visuals can be created alone or with a team. Because they are idea-driven they are often written on a whiteboard or similar surface, allowing for the incorporation of ideas of how to “find answers to nondata challenges” (Berinato, 2016).

The example below shows how they’re used to solve problems; in this case what should be done to increase sales.

(Roam, 2013)

This flowchart is from Dan Roam’s book The Back of the Napkin, which provides guidance on how to use visual thinking to generate ideas and solve problems.

“There is no more powerful way to prove that we know something well than to draw a simple picture of it. And there is no more powerful way to see hidden solutions than to pick up a pen and draw out the pieces of our problem” (Roam, 2013).


(Berinato, 2016)

Data-driven & Exploratory visualizations can be used in two ways: for confirmation of information believed to be true or for exploration of answers to specific questions. It’s often used by data scientists and business intelligence analysts (Berinato, 2016).

This type of visual discovery can be a catalyst for positive change that could have been otherwise overlooked.

The University of Illinois Chicago College of Pharmacy hired the Urban Data Visualization Lab (UDVL) to create a series of maps that could be used for their strategic planning.

UDVL took address data for locations of different types of colleges and different types of pharmacies throughout Illinois and geocoded them to create points on a map. Pharmacy and college symbolization was based on attributes supplied by the client. Illinois counties were symbolized based on the RUCA (Rural Urban Commuting Area) codes joined to county boundaries. Interstate highways and major water bodies were added for reference. Work was done primarily in GIS software” (College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago, n.d.).

This customized data visualization was reported as making the client’s strategic meetings “more productive” (College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago, n.d.).


“The goal is simple: give people factual information based on data that is, for the most part, not up for debate” (Berinato, 2016).

(Berinato, 2016)

Many Data-driven & Declarative visualizations are created with applications (like Excel) and are used for presentations. You’ve probably created a few line charts, bar charts, and scatterplots yourself; the trick is to keep it simple and focus on the point you are trying to make.

This Incubation Periods chart from data-journalist David McCandless is a great example of on point simplicity .

(McCandless, n.d.)

Based on data from the US Centers for Disease Control and Prevention and the World Health Organization, this beautiful line chart communicates the differences in incubation periods for various illnesses, highlighting COVID-19 in orange text for emphasis.

The message must be simple and able to be decoded easily by the viewer. Berinato says “A manager should be able to present an everyday dataviz without speaking at all” (2016).


CREATE YOUR OWN

Ready to get started? According to Berinato, you should begin by asking yourself two questions:

  1. Is the information conceptual or data-driven?
  2. Am I declaring something or exploring something?

The answers can help you choose how to display your information in a way that communicates your message effectively.

Four Categories of Data Visualizations

“Visualization is merely a process. What we actually do when we make a good chart is get at some truth and move people to feel it—to see what couldn’t be seen before. To change minds. To cause action. … But good outcomes require a broader understanding and a strategic approach” (Berinato, 2016 June 1).

until nxt time …

References

Berinato, S. (2016, June 1). Visualizations That Really Work. Harvard Business Review. https://hbr.org/2016/06/visualizations-that-really-work

Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press.

College of Pharmacy Strategic Planning | Urban Data Visualization Lab | University of Illinois at Chicago. (n.d.). Urban Data Visualization Lab. https://udv.lab.uic.edu/featured-projects/college-pharmacy-strategic-planning/

Marchese, C. (n.d.). Information Design Processes. https://quinnipiac.blackboard.com/bbcswebdav/pid-3358434-dt-content-rid-44419680_1/xid-44419680_1

McCandless, D. (n.d.). COVID-19 #CoronaVirus Infographic Datapack. Information Is Beautiful. https://informationisbeautiful.net/visualizations/covid-19-coronavirus-infographic-datapack/

Roam, D. (2013). The Back of the Napkin (Expanded Edition): Solving Problems and Selling Ideas with Pictures (Expanded edition). Portfolio.

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