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10 Types of Data Visualization

  • Vrinda Mathur
  • Jun 07, 2022
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Words do not always offer the most accurate image. Raw data may not always provide the best narrative. Visual information is incredibly appealing to the human mind. That is why data visualization is such an effective communication tool. But don't panic if "data visualization" sounds complicated and technical—it doesn't have to be.

 

This article will teach the foundations of data visualization in an easy-to-understand manner. There are several examples of various sorts of data visualizations and when to use them in your reports, presentations, marketing, and other projects.

 

 

What is Data Visualization?

 

Data visualization is the use of popular visuals to depict data, such as charts, plots, infographics, and even animations. These information visualizations explain complicated data linkages and data-driven insights in an easy-to-understand manner.

 

Data visualization may be used for a number of objectives, and it is crucial to highlight that it is not limited to data teams. It is also used by management to communicate organizational structure and hierarchy, and data analysts and data scientists utilize it to uncover and explain patterns and trends. 

 

According to Harvard Business Review, data visualization may be used for four different purposes: idea development, concept illustration, visual discovery, and everyday dataviz. 

 

Data Visualization is frequently used to stimulate idea production across teams. They are typically used at the outset of a project to promote the collecting of diverse viewpoints and emphasize the collective's common issues. 

 

While these visualizations are often rough and unrefined, they assist lay the foundation within the project to ensure that the team is aligned on the problem that they're attempting to address for key stakeholders. Data visualization Illustration aids in the communication of a concept, such as a strategy or method. 

 

It is most typically used in learning environments such as tutorials, certification courses, and centers of excellence, but it may also be used to depict organizational structures or processes, improving communication amongst the appropriate personnel for specific tasks. Gantt charts and waterfall charts are commonly used by project managers to illustrate workflows.

 

Data visualization tools now go beyond the charts and graphs found in Microsoft Excel spreadsheets, displaying data in more complex forms such as dials and gauges, geographic maps, heat maps, pie charts, and fever charts.

 

 

How Effective is Data Visualization

 

When communication, data science, and design come together, they produce effective data visualization. Data visualizations transformed crucial insights from complex data sets into something comprehensible and natural.

 

Edward Tufte, an American statistician and Yale professor, believes that good data visualizations consist of "complicated ideas expressed with clarity, accuracy, and efficiency."

 

To create an excellent data visualization, you must begin with clean, well-sourced, and full data. When the data is ready to be visualized, select the appropriate chart.

 

After deciding on a chart type, you must construct and personalize your representation to your desire. You don't want to include any extras that detract from the facts, so keep it simple.

 

The importance of Data Visualization is reflected by how human brains process information. Using graphs and charts to view vast amounts of complicated data sets is more convenient than analyzing spreadsheets and reports.

 

Data visualization is a simple and rapid technique to explain common notions. By making a minor alteration, you may try out a new outline. Data visualization technologies have been critical in democratizing data, analytics, and making data-driven perception available to employees across a business. 

 

They are simpler to use than previous versions of BI software or traditional statistical analysis applications. This guide to an increase in lines of business utilizing data visualization technologies without IT support.

 

Data visualization is a great tool for sharing and presenting information because it allows you to see the story behind the statistics. Data visualization may be used to demonstrate performance, communicate trends, assess the impact of new tactics, display relationships, and much more. 

 

These representations may be important instruments for communication and cooperation, adding value to reports, journalism, applications, and any other environment where information must be shared.

 

Also Read | Data Visualization Techniques


 

Types of Data Visualizations 

 

A good data visualization simplifies and shows information in such a way that you may concentrate on the most relevant details. Let’s go through some of best tools of data visualization :- 


Types of Data Visualization:- 1.  Line graph 2. Column Chart 3. Bar Graph 4. The Pie Chart 5. Dual- Axis Chart 6. Area Chart 7. Scatter Plot 8. Mekko Diagram 9. Pyramid Diagram 10. Bubble Chart

Types of Data Visualization


 

  1. Line Graph

 

The line graph is the most often used form of graph in many commercial applications since it easily and succinctly depicts an overall trend. A line graph (also known as a line chart) is a graph that is used to illustrate the values of something over time.
 

Your sales department, for example, may plot the change in the quantity of sales your firm has on hand over time. Straight lines link the data points that show the values. When should you use line graphs?

 

When it comes to displaying trends. When you wish to display trends for multiple categories over the same time period and so compare them.

 

  1. Column Chart

 

A column chart is one of the most frequent forms of data visualization tools. As you are aware, we were taught how to create column charts in primary school. Because they are simple to grasp, take less time, and compare diverse kinds of data. A column chart may also be used to track data sets over time.

 

A column chart often incorporates data labels as well as the horizontal (X) axis, with measured metrics or values displayed on the vertical (Y) axis, commonly known as the chart's left side. The Y-axis is typically set to zero and extends as far as the greatest measurement being tracked.

 

  1. Bar Graph
     

You may alternatively use a bar graph. A bar graph and a column chart are used interchangeably. Column charts, on the other hand, limit your label and comparison space. As a result, using a bar graph is always an excellent choice.

 

When working with a longer label, displaying negative values, or comparing 10 or more items, a bar graph should be used. In these circumstances, the data label will be on the Y-axis, while the measurements will be on the X-axis.

 

A bar graph also allows us to compare data sets from various groups at a glance. The graph simply depicts the categories on one axis while producing a discrete value on the other. The purpose over here is to demonstrate the relationship between two axes, and bar charts may also demonstrate major changes in data over time.

 

Furthermore, it is an efficient method of comparing objects from distinct groups. The bar graph compares quarterly results over a four-year period.

 

  1. The Pie Chart

 

Pie charts are visually appealing data visualization styles. They are simple to read and are used to depict relative sizes. A Pie Chart is a circular graph that displays data relative sizes using "pie slices."

 

Since it depicts each element as part of a whole, a pie chart is an excellent choice for displaying percentages. The complete pie indicates one hundred percent of the total. The pie pieces symbolize different parts of the total.

 

When should you use a pie chart? When you wish to indicate the percentage of the total that each number possesses. When you wish to demonstrate how a group is divided into smaller components.

 

  1. Dual-Axis Chart

 

Data visualization charts typically include a single y-axis or x-axis. A dual-axis or multiple axes chart, on the other hand, employs two axes to rapidly depict the relationship between two variables with distinct magnitudes and measurement scales.

 

When integrating many charts and adding a second y-axis for comparison, you should use a dual-axis chart. This kind allows you to readily observe two variables with significantly different scales. It is also much simpler to observe them within the same graph rather than switching between two charts.

 

Also Read | Applications of Data Visualization

 

  1. Area Chart

 

An area chart is an excellent way to exhibit data that demonstrates a time-series connection. An area chart is a sort of chart that shows how one or more quantities change over time. It resembles a line graph.

 

Data points are joined by a line in both area charts and line graphs to represent the value of a quantity at various periods. They are both useful for displaying trends. The area chart, on the other hand, differs from the line graph in that the region between the x-axis and the line is colored. Thus, area charts provide an idea of the whole volume.

 

  1. Scatter Plot

 

The scatter plot, also known as a scatter diagram, scatter graph, and correlation chart, is another common data visualization form. Scatter plots are useful in many fields nowadays, including business, biology, social statistics, data science, and so on.

 

A scatter plot is a graph that depicts the association of two variables. The goal is to demonstrate how much one variable influences another. When there is a relationship between two variables, the first one is usually referred to as independent. The second variable is referred to as dependent since its values are determined by the first variable.

 

  1. Mekko Diagram

 

Mekko charts are another unusual data visualization format that you may not be familiar with. You may only be aware of it if you work in the data analysis sector. A mekko chart has a layout similar to a stacked bar graph.

 

There is one notable exception. The X-axis monitors another dimension of your data sets rather than monitoring time progress. And, using the Mekko Chart, you can simply compare values, measure them, and determine their composition. You will also be able to study data distribution at the same time.

 

  1. Pyramid Diagram

 

Pyramid graphs are aesthetically pleasing and fascinating graphs. Furthermore, they are one of the most simple data visualization kinds and methodologies. It is a graph shaped like a triangle or pyramid. It is more effective when displaying a hierarchy. The pyramid levels are arranged in a progressive order, such as:

 

From most important to least crucial For example, CEOs at the top and temporary workers at the bottom. From most specific to least specific. For example, expert fields should be at the top, while ordinary fields should be towards the bottom. From old to modern.

 

  1. Bubble Chart

 

A bubble chart, like a scatter chart, may demonstrate correlations and distributions. In these versions, the data points will be replaced by bubbles. 

 

A Bubble Chart is also a multivariable graph that is a hybrid of a Scatter Plot and a Proportional Area Chart. Bubble charts, like scatter charts, employ a Cartesian coordinate system to depict points along a grid where the X and Y axes are independent variables.

 

In contrast to Scatterplot, each point is labeled or classified. Furthermore, the area of each plot point's circle reflects a third variable. Bubble charts are commonly used to compare and demonstrate the relationships between classified circles. It may also be used to analyze patterns and relationships.

 

Also Read | Types of Data Visualization

 

Data visualization techniques are essential components of data analysis because they efficiently summarize enormous volumes of data in an understandable graphical format.

 

There are several data visualization types, each with its own set of advantages, disadvantages, and applications.

 

The most difficult step is deciding on the best picture to convey your facts. Several elements influence your decision, including the type of conclusion you want to reach, your audience, critical KPIs, and so on.

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