Breaking Down the Four Types of Data Viz

When first beginning to discover the intricacies of Data Visualization, it can be exciting to jump in and begin designing. So much can be learned visually, especially when trying to implement data to represent a meaning and showcase a problem. As David McCandless describes it in his Ted Talk “The Beauty of Data Visualization”, Data is the new oil and can be a ubiquitous resource that can be easily shaped and mined. As we explore data visualization further, it’s crucial that we understand the different ways in which we can choose to visualize data. But before we go any further, a good way to start any good chart (Good Charts) is by considering two primary questions:

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

These two questions will help determine which one of the 4 main types of Information Visuals might be best to create for your specific data: Conceptual-Declarative Information Visuals, Conceptual-Exploratory Information Visuals, Data-driven exploratory information visuals and Data Driven-Declarative Information Visuals.

Conceptual-Declarative

• A primary way to use these visuals is when you have an abstract idea or concept but want to present it in a clear and concise way. These visualizations usually demand clear and simple design but often lack it. These visuals don’t face constraints imposed by axes and accurately plotted data in the way that many other charts do. An example of this type of visual is an anatomy chart of the human brain. This design shows the names and locations of the parts of the brain but not necessarily how they work. 

Conceptual-Exploratory

• Out of the four, conceptual-exploratory visuals are the least intuitive of the bunch. These visualizations are typically used to help the audience explore and analyze relationships between different concepts or ideas. One bonus of these visualizations is that they can be done alone or in a collaborative setting. The resources required for this type are also inexpensive and easy to produce. An example is building your first mind map from a brainstorm session. One assignment that I recently worked on for a previous ICM course required a mindmap. A place to generate ideas with not too many bells and whistles.

Data-Driven Exploratory

• Contrary to conceptual visualizations, this type of visualization helps us identify trends and patterns. Data-Driven Exploratory visualizations are used to explore and analyze large data sets. David McCandless discusses about visualization called the “Billion Dollar-oGram” which he created to help visualize the relative size of the world phenomena that we see everyday in the news at a more digestible scale.
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Data-Driven Declarative

• Also called Everyday Data Viz. This type of visualization is used to present data in a clear and concise manner. Most often, these visuals have the ability to communicate key insights and trends in the data to our audience. An example of this would be these simple charts displaying various stats of Lebron James, one of the top basketball players in history. 

Conclusion:

It’s important to note that although these four types and 2×2 charts are a great way to start your data visualization journey. These are not hard rules that must be followed, data visualization in itself is a creative practice. It’s clear that these visualizations take a huge amount of work due to the amount of data that has to be crammed into such a small surface area. When designing a visualization, it’s always important to keep your original intentions in mind and who you are trying to communicate with. Before embarking on your designing journey, here are 5 tips to keep in mind from the Good Charts.

1. We don’t go in order.
   1. A chart reader might jump around and pacing is completely different
2. We see first what stands out
   1. Our eyes go straight to change and difference (peaks and valleys)
   2. Titles aren’t usually the first thing a chart reader sees
3. We see only a few things at once
   1. The more data, the more singular the chart’s meaning becomes
4. We seek meaning and make connections
   1. Seeking sense immediately makes us process visual info thousands of times more efficiently than text.
5. We rely on conventions and metaphors
   1. We think this map is upside down because we learned north is up even though there is no up or down for a planet.