A graphic was prepared by Joseph Minard about Napoleon’s Russian Expedition, which ended in defeat in 1869. By looking at this graph, it was possible to obtain information such as the decreasing size of the army, the position of the military, including where some units left and then rejoined, the direction of movement, and the temperature at various dates during the withdrawal from Moscow. Edward Tufte, also known as information design and considered a pioneer in information design, says that this graph is the best statistical graph ever drawn. He then adds that while this is an admirable work, it can be found one in a million and that there is no compositional principle for how such a graph can be created.

Fortunately, there are some design processes we can refer to follow a routine methodology in data visualization.

Suitable visualization methods provide invaluable tools to discover, understand, and explain data.

What is Data Visualization?‍

Today, in the age of Big Data, this old saying makes more sense than ever before. Data visualization aids storytelling by clearly communicating complex issues and plays a crucial role in identifying key insights from data to streamline this process.

Why Visualization?‍

Visuals make analysis more accessible and faster while giving you the ability to see essential topics at a glance.

Most people respond much better to images than text. Ninety percent of the information sent to the brain is visual, and the brain processes image 60,000 times faster than text.

Visualization is an excellent option to discover interesting data points.

Data visualization lets you animate data and tells the story of the hidden insights among the numbers.

These points strongly highlight the importance of using data visualization to analyze and communicate information.

Types of Visualization

In everyday life, concepts such as data visualization and infographics are often used interchangeably.

Infographic:

• It is drawn manually, and therefore the data is specially processed.
• Infographics tend to be aesthetically rich due to manual creation processes.
• It is explicitly created for existing data, and therefore it is trivial to recreate it with different data.
• In other words, infographics are images in which the data representation is manually edited or drawn.
• Changing or updating data in an infographic is difficult as any changes have to be applied manually.
• Aesthetically rich (solid visual content that stands out and engages) and relatively data-poor (because every piece of information must be manually coded).

Dataviz:

• It is generally sterile from an aesthetic point of view.
• Relatively data–rich (unlike infographics, large volumes of data are viable).
• It is algorithmically drawn and may have unique touches but is primarily created with computerized methods.
• Easy to recreate with different data (the same form can be reused to represent different datasets with similar dimensions or properties).

Designer–Reader–Data Triangle

An effective data visualization has three pillars: The designer, the reader, and the data. Each of these three legs forms a unique relationship with the other two. While each visualization project must consider the needs and perspectives of all three, the dominant relationship ultimately determines which visualization category is needed.

Visualization Categories

Exploration Versus Explanation

We can talk about two data visualization categories: Exploration and Explanation. Because the two serve different purposes, some tools and approaches are only suitable for one and not the other. Therefore, it is essential to understand the distinction so you can make sure you are using the appropriate tools and approaches for the task at hand.

The primary function of data visualization is to move information from point A to point B. In exploratory visualization, point A is the dataset, while point B is the designer’s mind. In descriptive visualization, point A is the designer’s mind, while point B is the reader’s mind.

Exploratory Visualization‍

We use exploratory data visualizations when we have a lot of data and aren’t sure exactly what’s in it. It turns the dataset into a visual environment, helping to identify its properties quickly. But if you oversimplify or omit too much information, you might miss something important. These visualizations are part of the data analysis phase and are used to find the story the data has to tell you. Exploratory visualizations allow viewers to explore and create their interpretations rather than be descriptive. Examples of this are digital and interactive visualizations.

Descriptive Visualization

We use descriptive data visualizations when we already know what the data should say and are trying to tell that story to someone else. Regardless of the target audience, if the story you are trying to tell – or the answer you are trying to share – is known to you initially, you can make design decisions to highlight this story directly.

This requires you to make certain editorial decisions about which information stays in and which is distracting or irrelevant and should come out.

We can see exploratory data visualization as part of the data analysis phase and explanatory data visualization as part of the presentation phase.

Exhibition Visualization

In–display visualizations, viewers, try to interpret meaning by relying on their capacity to perceive and translate the features of the visualization.