Nowadays, data is used as fuel to make informed decisions in businesses and organizations. Generally, the data collected from any source is unstructured and noisy. Data analysts and scientists perform data cleaning and preprocessing to gain valuable insights from the input data. In this article, we will discuss the data visualization wheel and its importance in data visualization tasks.

What is Data Visualization?

We can define data visualization as the process of representing data in a graphical representation. The graphical representation can be a bar chart, a scatter plot, a map, or any image that can be conveniently used to represent data in an understandable manner. 

Data visualization helps us understand and retain information easily. We can visualize patterns and relationships between different objects easily using a data visualization tool such as a graph or a map. For instance, consider the following image.

In the above image, different characteristics of electromagnetic waves have been shown. In a single image, various aspects of electromagnetic waves such as their wavelength, frequency, approximate scale, the penetrability of earth’s atmosphere, and the temperature of the black box objects at which a particular wavelength is emitted with the highest intensity have been discussed. 

If this image isn’t used, the information shared in the image can take an entire blog or multiple pages of a book. This is one of the aspects of why data visualization is so important. 

Visualization of data in a proper manner is also important. Otherwise, the representation can become messy, hard to remember, or even obsolete. Therefore, we need to keep certain aspects of data visualization in our mind. 

Alberto Cairo, in his book “The Functional Art”, described a data visualization wheel that discusses tradeoffs between different elements of data visualization. In this article, we will try to understand the data visualization wheel.

What is the Visualization Wheel?

The data visualization wheel is a tool that depicts the tradeoffs between different aspects of a visualization. The visualization wheel looks as follows.

The visualization wheel in the above figure shows 12 aspects of a data visualization. The upper half of the visualization wheel represents aspects related to complex and deeper visualization of data. The lower half of the wheel represents more accessible but shallower visualization of data. Let us discuss all the aspects one by one.

Abstractions vs Figuration

The figuration of a visualization depends on the phenomenon that is being represented through the visualization. For real phenomenon having physical representation, we use components like graphs and drawings for visualization. We can say that visualizations with more figuration use physical representations such as drawings and photographs to show the data. 

On the other hand, if the phenomenon being represented in the visualization is more conceptual, abstraction is preferred over visualization. Thus, visualizations with more abstraction use conceptual and less real representations of a given phenomenon. 

Functionality vs Decoration

A visualization with no decorations which is a direct representation of data is called a functional graphic. In other words, the visualization uses minimal components to completely describe the data. Thus, functional visualizations have no decoration and they are direct visual representation of information.

When we use decorations in a visualization, the visualization may be artistically embroidered. Hence, the viewer may spend time looking at the visualization. This might increase the familiarity and memorability of the visualization for the user.

Density vs Lightness

If a visualization shows a large amount of information, it is said to be dense visualization. A dense visualization shows in-depth information and needs to studied in depth. Dense visualizations are suitable for situations where the viewer is highly engaged. For instance, you can use dense visualizations in research journals and book. 

On the other hand, a lighter visualization shares overview of an information instead of getting into depth. A light visualization is used in cases where the viewer isn’t very much interested in engaging with the visualization and will just have a look on the image and move ahead. Light visualizations are only used to add value to the text in many cases. For instance, you can use visualizations with lightness in magazines to show any trend.

Multi-Dimensional vs Uni-Dimensional

A multidimensional visualization takes into account more number of dimensions of any phenomenon. It helps the viewer to understand different aspects of the phenomenon from a single image. Thus, the viewer gets understands the phenomenon as a whole.

On the contrary, a uni-dimensional visualization focuses on a single aspect of a phenomenon. You can say that a multidimensional visualization helps the viewer explore a phenomenon in multiple ways whereas a uni-dimensional visualization limits the exploration to limited aspects.

Originality vs Familiarity

There are different types of graphical tools such as bar charts, scatter plots, line charts, etc.  A particular type of data and the information that we need to show in a visualization requires a specific type of graph. Hence, we need to choose a graphical representation that fits well for any given situation. 

Original graphics generally don’t conform to the common visualization patterns that we know. On the other hand, visualizations with higher familiarity use common graphical representations for data that we are familiar with.

Novelty and Redundancy

If we show same information many times in a visual, we introduce redundancy in the visualization. For instance, consider the following bar chart. 

Here, we have named all the bars on X axis with subjects names. Additionally, we have also used legends to specify the subjects that are being represented by the bars. Hence, the legend is redundant.

Introducing novelty in the visualizations helps us describe each phenomenon is a single way.

Thus, we can say that redundant visualizations use multiple ways to represent a single quantity or phenomenon while novel visualizations use only one element to show a single phenomenon.

What Components of the Visualization Wheel Should You Use?

How you represent data in a visualization completely depends on who the target audience is. If you are going to present a visualization in a scientific conference, to an engineer, to a data scientist, or to anyone who is going to understand the visualization by looking at the details, you can use a visualization with the visualization wheel components such as density, multi-dimensionality, functionality, and abstraction.

If your target audience isn’t very much into the details, you can use the data wheel components such as decoration, figuration, and familiarity to make the visualization understandable and memorable within a short span of time. For instance, if you are going to present data to artists, politicians, journalists, graphic designers, etc, you can use the components given in the lower half of the data visualization wheel.

Conclusion

In this article, we have discussed the data visualization wheel in detail. Data visualization can be a crucial aspect in any presentation and using different components from visualization wheel can help you ace any visualization task. To learn more about data analysis, you can read this article on data cleaning. You might also like this article on data analyst vs data scientist.

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