What is Data Visualization: Definition, Tools, and Common Errors

Data visualization is a branch of data science that visually represents the data and information collected, thus transforming raw data into valuable information.

Its enormous usefulness facilitates understanding information using graphs, maps, diagrams, figures, and colors. In short, data visualization allows information to be more accessible and within reach of more people.

For example, data visualization is tremendously helpful in making better decisions in the business world.

Such is the value and usefulness of data visualization, and many tools related to data analytics, especially those intended for non-specialized users, include data visualization functionalities.

Below, we show you the different types of data visualization that exist and how you can put this branch of data analysis into practice, but only by giving you some valuable tips so that you make as few mistakes as possible.

Types of Data Visualization

Data visualization is divided depending on the graphic resource used to display the data. In that sense, there is a wide variety of formats, but below, we will tell you about the most popular and most commonly used ones.

Graphics

It is the most widespread form of data visualization because it is intuitive and easy to understand.

Graphs represent data using lines, bars, points or figures.

Diagrams

The most famous and popular diagram in the world is the family tree. In that sense, diagrams usually show the relationship that exists between all of them or a specific process rather than showing the data.

Therefore, while a graph can visually indicate the percentage or quantity of an element, a diagram shows how they influence each other, how they have been transformed, or how they evolve. Diagrams can also be used to indicate quantities.

Maps

Maps are used when data related to geographic, political, or sociodemographic information is to be represented and when differences between continents, countries, or locations are to be shown.

The best-known are color maps, in which a tone is assigned to each numerical value or numerical strip, and the part of the map identified with it is colored.

Other prevalent ones are also animated maps and point maps.

Tips for creating compelling data visualizations

When representing the data, we must consider several issues so that the public understands what we want to convey as best as possible.

It is common for the different visual resources obtained from data visualization to be presented in a presentation or meeting.

• Provide context: Graphs, maps, or diagrams are essential in any data visualization process, but it is just as important to provide the necessary context to understand the data perfectly and avoid creating preconceived ideas. This context, of course, will not be transmitted visually but rather with text or orally at the time you consider most appropriate.
• Choose the type of data visualization carefully: We have previously explained the three most popular types of data visualization, although there are many more. Each of them represents one type of data better than another. For example, geographic data is best described by maps, numerical data by diagrams, and temporal data by graphs. Be careful because choosing one or the other can make your representation a success or create more confusion than solutions.
• Be clear about your target audience: To understand what we mean by this point, remember that you do not explain things the same way to a child as to a teenager or an adult. In that sense, data visualization must be adapted as much as possible to the audience that will see it. For example, suppose the audience to whom you will present the data is someone other than an expert. In that case, it is better to represent the information as simply and generically as possible. On the other hand, if the audience is more specialized, you can add more details and specifications.

Common Data Visualization Mistakes

These are the most common errors when presenting a data visualization. Remember them during your data analysis process, and we also tell you how to avoid them.

Too much information

No matter how much data you have, you must be selective with the information you decide to capture in your data visualization presentation. Otherwise, you may overwhelm your audience, and they may need help understanding the intention and objective of your presentation.

No matter how attractive all the data you have collected is, select only those that directly affect the central topic of your presentation. If you find it very difficult to get rid of some, you can dedicate an extra presentation section to show them once you have finished exposing the central part.

Little color contrast

Although you may see it as a minor detail, colour is one of the essential elements in data visualization. It allows the public to differentiate different data and classify it in their minds quickly. Furthermore, even though you may not believe it, colours help a lot in memorizing concepts.

For these reasons, using very similar colours will complicate understanding your data visualization.

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I recommend that you do not use different shades of the same colour. Still, other colours assign each piece of information or concept a colour that defines it, although the latter may be subjective.

Excessive use of colour

Neither too much nor too bald. Although colour is essential in data visualization, you should stay moderate. Using too many colours can overwhelm the audience and cause them not to differentiate the information. In short, an excess of colour has the same effect as an excess of information.

To avoid this, capture only a few variables or factors in the same graph, diagram or map; select only those that will add value and that matter.

You can also group different data that have some similarity under the same variable and thus reduce the number of elements you must colour.

This is one reason people are grouped into different age groups in the graphs (18-25, 26-45, and 65+) and why a bar is not used for each age group.

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