What Is Data Visualization? Definition, Types & Examples

Some humans are easier to understand or digest something visually. One example is within the scope of the company, appropriate and accurate decision making must be based on data. In order for the data to be easily understood, data visualization was used.

So, what exactly is Data Visualization? What are its uses and types? Check out the following explanation.

What Is Data Visualization

Data Visualization or data visualization is a series of processes displaying data or information in an easy-to-understand form, such as graphs, numbers, charts, and so on. In short, visualization simplifies data sets to display.

In its application, using visual elements aims to make it easier for readers to see and understand trends, outliers, and patterns in data. Visualization allows decision makers to see the analytics presented visually. Thus, they can understand difficult concepts or identify new patterns. That way, strategy making and decision making becomes more precise.

Uses of Data Visualization

Finding and gathering vast amounts of information is not easy, which is the reason why data visualization is so important in business.

Of course, presenting data in raw or written form will make it difficult for the team, especially the decision makers or company leaders, to know what it means.

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Data that is displayed visually allows many people to understand the data more quickly. Decision makers can easily see and understand the work, based on the variables they have.

Types Data Visualization

  1. Temporal

Type This type of data visualization is often found. Temporal is usually used to show the results of a data series that is linear, or one-dimensional. This type of visualization is a line, which starts and ends at a certain point. Examples: Timeline, time series, and line charts.

  1. Hierarchy

This type of hierarchy is used to show the relationship between one group to another larger group. Example: Tree Diagram.

  1. Network

Dataset or a collection of data that influence each other. The use networks in data visualization facilitates the relationship between datasets . Example: Node-link diagram.

  1. Multidimensional

This type visualizes data that has many variables or dimensions. dataset that is displayed a lot makes the visualization more attractive so that it is easy to understand. Example: pie charts and histograms.

  1. Geospatial

This type of geospatial represents the real form of an object, or space that has data to display. Example: Heatmap and cartograph.

How Data Visualization Works

So, how does data visualization in practice, which can provide benefits for a business, both large and small? Here are some pointers that hopefully enlighten you:

  • You can take advantage of pictures, graphs, or tables that will make it easier for you and your team to understand all the incoming information, as well as find new trends that are useful for growing your business, such as trends social media marketing, content, YouTube marketing trends, and so on. 
  • Can help reduce the time and effort spent on data analysis. It saves time, saves energy, and is more fun to do.
  • Very useful for analyzing a wider range of user groups, including sales, as well as marketing teams, as well as finance.
  • It is possible to make much faster decision-making when designing, or revising key business strategies, to finally be able to take the best business action.
  • Capable of providing increased ROI of your business from all that data.

Why is Data Visualization So Important? 

When viewed from a biological level, it is very important to understand the concept of data visualization. This is because it is related to how the human brain works in processing information. 

Instead of relying on numerical reports alone, the graphical representation of all the data that has been entered, both in the form of charts and graphs, has indeed become easier for you to understand.

When viewed from the company level, by utilizing the way data visualization it allows companies to:

  • Can identify business strengths and areas for improvement.
  • Determine various other factors that influence online customers’
  • Finding the right strategy in product placement and pricing.
  • Can predict future trends, including product sales volume.

The following is a summary of the core concepts that explain how data visualization, similar to what Edward Tufte and Stephen Few, two data visualization figures, have identified, namely:

  • clarification

should set a clear goal for others to care about.

  • Simplify

So, only the visualization style that is most suitable for the type of data being analyzed will be displayed.

  • Compare

Should display comparisons side by side for easier absorption.

  • Present

Must draw the viewer’s attention to an important or relevant piece of data.

  • Explore

Create visuals that can lead viewers to discover new things, not just answer certain questions.

  • View Multiple Data

Try enabling multiple views derived from the same data. The goal is to be able to find various insights that are seen from different perspectives.

  • Ask Why

You should ask why something happened, don’t just note that it’s happening right now.

  • Be Skeptic

It’s a good idea to encourage more questions than to accept the simple answers that the previous questions provide.

  • Respond

You will share the data that has been found to get alternative perspectives and can build collaboration.

  • Details

Try to make large data sets coherent and can break down data into multiple levels of more detail.

  • Validation

graph data visualization should be able to speak for itself, while providing access to backup information and some raw data as a proof point


This is now a very messy data era due to the large number of data sources available. With data visualization , it can provide extraordinary abilities for business leaders and functional heads to extract useful and relevant information so that it can have an impact on the strategies and business plans they are doing.