Information Visuals and Exploring Data
Graphs, charts, infographics, diagrams: these are all methods used to present data visually, allowing viewers to understand its meaning quickly and easily. When creating these visuals, however, it is important to know what kind of data you have and what you are trying to achieve with the information. This will allow you to choose the most effective visual for your situation.
Conceptual or Data-Driven
Before deciding how the visual is going to look, it is necessary to know the kind of information you have. Conceptual information is qualitative, consisting of abstract ideas and concepts that express meaning. Data-driven information is quantitative, involving specific numbers and statistics that tell a story.
Declarative vs Exploratory
After understanding your information, you must determine what the goal of the visual is. Declarative visuals make a statement. They are formal presentations that “declare” a fact or idea. Exploratory visuals “explore” the data. They are more informal attempts to discover patterns and trends and provide answers.
The Four Types
The two pairs of attributes can be overlapped to create four types of information visuals that serve different purposes.
Conceptual-Declarative
A conceptual-declarative visual presents an idea. It uses simple and clear graphics to communicate a message and explain a concept.
This illustration about the Design Thinking Process from the Nielsen Norman Group is an example of a conceptual-declarative visual. There are no statistics or numbers, rather, it uses concepts like emphasizing and ideating to outline the structure of the process. It is representing the idea of “design thinking” with simple shapes along with arrows that show its iterative nature.
Conceptual-Exploratory
A conceptual-exploratory visual is an informal drawing used to come up with ideas. Teams can create multiple of these to explore various ways to approach a topic. The best one can then be taken and turned into a formal conceptual-declarative visual to present to others.
These sketches by Lara Gülbüke Kınay show conceptual-exploratory visuals from a common ideation exercise called Crazy-8s. This exercise involves pieces of paper folded into eighths and requires people to quickly sketch ideas for solutions with only one minute per box. As shown in the image, these drawings are informal and unpolished. The main goal of conceptual-exploratory visuals is to lay out ideas in a visual way that makes them easy to understand and discuss.
Data-Driven-Declarative
Data-driven-declarative visuals are also known as “everyday dataviz” because they are the most common type and what people usually think of when they think about statistics. They include graphs like line, bar, or pie charts and declare a clear and simple message.
This chart from the BBC shows the amount of plastic in the ocean from 2005 to 2019. With a glance, readers can see the rising line means it has drastically grown and quickly understand the unfortunate message. As a data-driven-declarative visual, this graph uses plain imagery to convey facts and tell a clear story.
Data-Driven-Exploratory
Finally, a data-driven-exploratory visual uses data to discover trends and patterns or confirm connections. If it leans more toward the “confirmatory” side, the visual will prove or refute a hypothesis you had about the data. If it is more “exploratory,” the visual is open-ended and may have interactive components that let you adjust variables and observe the outcome. Experts like data scientists can be brought in to help navigate these complex systems and uncover valuable insights.
This cluster diagram by Acquia shows data points plotted on a chart of “average order size” to “average days between purchases.” There are two groups or “clusters” of related dots that present a pattern; as Acquia says, “the more frequently a customer buys, the less they spend. Contrarily, the longer the buying cycle, the higher the spend” (Acquia). In a real business, a data-driven-exploratory visual like this could help managers understand their customer habits and where the most profit is coming from.
