Explaining Four Quadrants of Data Visualization Charts

Dana Fusek “Charts by Quadrants”

Data comes in all kinds of shapes and forms. We can often get lost in the purpose of the data if we do not have a good visual of the information. Therefore, we need to focus on the goal of the data.

When putting together data visuals, we need to understand our purpose in presenting data. Two main questions we need to ask are if we are declaring something (affirming information) or exploring something (discovering based on iteration, prototyping etc.)

This is also referred to explanatory data vs. exploratory data. Are we encouraging our audience to explore their own data? Or are we explaining data that has already been analyzed? (Marchese 2020)

Another question we need to dapple on is whether the information we are gathering is conceptual or data-driven.

To help visualize data and process information, we can identify the best way to represent our data by sorting it into four different quadrants. (Berinato 2016)

Scott Berinato “Good Charts: the HBR Guide to Making Smarter, More Persuasive Data Visualizations

Conceptual-declarative (Idea Illustration)

The main goal of conceptual-declarative charts is learning and simplifying complex information and is used about 30% of the time compared to the other three chart types.

Conceptual-declarative charts are used to present ideas and information to an audience in a simplified manor, usually though metaphorical visuals such as: pyramids, decision trees etc. (Berinato 2016)

Take for example a standard organizational chart within many standard businesses.

This conceptual-declarative chart represents the parts of an organization, position relationships, and a highest to lowest “ranking system” (highest on the top, lowest on the bottom.)

Conceptual-exploratory (Idea Generation)

In conceptual-exploratory data our goal is to discover, simplify, and learn. Conceptual-exploratory charts make up about 15% of chart types.

These charts are based on key aspects of design thinking and are practiced mostly through collaboration. From here we are beginning to explore ideas and innovations through sketches in a piece of paper (or napkin!), drawings on a whiteboard etc. (Berinato 2016)

Over the winter, I participated in a “Crash-Course in Design Thinking” by the Dschool for a graduate school course. We were assigned a partner and asked to explore a very broad and conceptual idea: redesign the gift giving process. Our goal by the end of a 90-minute session we had to complete interviews, create problem statements, sketch new ideas, and form one big idea.

Through multiple interviews with my partner (whom I had just met), we began opening up to each other about the memorable gift giving experiences. She told me about an architecture book she had given her 10-year-old daughter, who loves arts and crafts, in hopes of bonding through creative and visual experiences.

My sketches below represent the conceptual-exploratory process and the “big idea” by the end of the workshop.

Data-driven-declarative (Everyday Viz)

The goal of data-driven declarative data is to affirm an idea though data trends. These charts are used the most of all four chart charts and are used about 50% of the time. (Berinato 2016)

These charts are used in what is referred to as “everyday data” and used overwhelming to give  information based on data. Unlike conceptual data, this concept is based on facts and does not leave room for debate.

Take for instance David McCandless’s visual for a timeline peak break-up times according to status updates. Based on scraping 10,000 status updates, McCandless and his partner were able to find patterns of when people indicated “break up” or “broken-up” statuses. He noted the highest peaks of data happening before spring break and two weeks before Christmas, with the lowest peaks happening on Christmas day. (McCandless 2010)

Data-driven-exploratory (Visual Discovery)

Finally, data-driven-exploratory charts are used the least of the four types, only being used about 5% of the time. However, they are equally important. Data-driven-exploratory charts are used for trend spotting, deep analysis, and sense making.

Data-driven-exploratory charts really split into two categories based on if you are attempting to explore or confirm information:

Visual exploration– exploratory and uses data to see patterns or trends emerging.

Visual confirmation– declarative and meant to test a hypothesis (Berinato 2016)

Take for instance this example from an article titled, “Visualizing your Exploratory Data” by Thomas Plapinger. The chart below “Dew Point vs. Average Temperature” shows a strong correlation between the two variables and upward trends. Palpinger says,

“We can see in the first plot that as the temperature rises so does the dew point which while unsurprising once you learn that Dew Point is directly related to Humidity”

(Palpinger 2017)

If this information was presented in a hypothesis that Dew Point is directly related to humidity, this chart could be used as visual confirmation.

References

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

Marchese, C. (2020). Information Design Processes. Reading.

McCandless, D. (2010, July). The beauty of data visualization. Retrieved July 19, 2020, from https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en

Plapinger, T. (2017, September 26). Visualizing your Exploratory Data Analysis. Retrieved July 18, 2020, from https://towardsdatascience.com/visualizing-your-exploratory-data-analysis-d2d6c2e3b30e

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