The Four Information Visuals

Our world is highly data driven, and the ability to communication information effectively has become vital. While traditional textual data is valuable, using graphics and images can significantly enhance data comprehension. Visuals offer simplified representations of complex facts and data, making them easier to understand. There are four types of information visuals to choose from, each with its unique strengths and applications.

Hi! My name is Kristin Ardese and I am a professional Graphic Designer and Marketing Strategist. I hope that by sharing some of my expertise, I can help offer valuable insights and build an engaging community.

Conceptual-Declarative

These visuals are primarily used to illustrate qualitative information. They encompass various forms such as flowcharts, infographics, and illustrative diagrams. Although these visuals might not involve data or statistics, they can easily explain steps in a process, outline key features of an event, or show hierarchy in an organization. The following example illustrates a conceptual-declarative visualization, presenting qualitative information in an illustrative manner.

From Mind Tools "The Simplex Process"

Conceptual-Exploratory

This type of visual is specifically designed to spark curiosity and facilitate the user’s concept comprehension. They often encourage experimentation and brainstorming to generate ideas and explore data trends. Mind maps are a great example of this type of visual. Below is a mind map that demonstrates idea generation within mobile design.

From Pencil Or Ink "Mind Mapping for Beginners"

Data-Driven-Declarative

When thinking of data visuals, data-driven-declarative are probably the most familiar. These are designed to present specific data and statistics in a clear and effective way. This quantitative data will most likely be displayed in formats like charts, graphs, and tables. Below you’ll find a few examples of how data-driven-declarative visuals can take shape.

From Kristin Ardese "A Lifetime Spent"

Data-Driven-Exploratory

These visuals are used to help explore large or complex datasets that sometimes focus on confirming a hypothesis. Often times these visuals are interactive, allowing users to filter and manipulate data that uncovers patterns. You may see examples of these in heatmaps, scatter plots or network graphs. Among data-driven-exploratory visuals, the scatter plot below serves as a prime example. It effectively showcases a large dataset such as population density and when interactive could help in the discovery of important patterns.

From Andy McDonald "Using Plotly Express to Create Interactive Scatter Plots"

Final Comments

By harnessing the potential of these four distinct information visuals, we can effectively communicate powerful information. Whether we are presenting business strategies, or expressing scientific findings, the strategic use of visuals promotes comprehension for all. While textual data retains its value, integrating graphics and images can elevate communication and deepen understanding.