What is Data Visualization and examples?

 

 

It's more crucial than ever to have accessible tools to view and comprehend data in our increasingly data-driven environment. After all, employers need more and more individuals with data skills every year. Data must be understood by both employees and business owners at all levels.

 

Data visualization is useful in this situation. To make data more accessible and understandable, data visualization in the form of dashboards is the primary tool for analyzing and sharing information for many businesses.

 

To detect links and patterns in the data, data visualization involves displaying substantial volumes of data in the form of graphs, maps, charts, and other visual representations. Although the phrase "data visualization" may create images of intricate graphics that only data scientists can comprehend, the practice is actually much more inventive and simple than you might imagine. Data visualization comes in various forms, including corporate dashboards, gastronomy, politics, and pop culture trends. Along with data analysis skills, effective data visualization demands strong graphic design and storytelling abilities.

 

We'll talk about some of the most famous data visualizations and how they function in this blog.

 

 

The graphical display of quantitative information and data using visual components like charts, tables, and maps is known as data visualization.

 

Large and small data sets are transformed into graphics that are simple for people to comprehend and process through data visualization.

 

Data outliers, patterns, and trends can be easily understood with data visualization tools.

 

To evaluate massive amounts of data in the big data age, data visualization techniques and technologies are necessary.

 

Data visualizations are employed to identify trends and information that are unknown. Visualizations that depict changes over time can be seen as line graphs. Bar and column charts are useful for comparing data and tracking relationships. To illustrate the components of a whole, a pie chart is excellent. The greatest way to graphically share geographic data is through maps.

 

Today's data visualization tools go beyond the tables and graphs used in the Microsoft Excel spreadsheet to display data in more sophisticated ways such as dials and gauges, geographic maps, heat maps, pie charts, and temperature graphs.

 

 

Data provided visually can aid individuals in understanding it more quickly and easily while data delivered in word format can be confusing (not to mention boring).

Finding patterns, trends, and correlations that could otherwise go unnoticed is made possible via data visualization.

 

 

Data visualization can be static or interactive. For centuries, people have used visualization of static data such as graphs and maps.

Interactive data visualization is a bit newer: It enables users to interactively alter what data they see and how it's handled by delving into the gritty details of these graphs and charts using their computers and mobile devices.

 

 

You may also hear the phrase "time series visualization" in addition to static and interactive data visualization. The term "time series visualization" refers to graphics that track data or performance over a period of time.

 

This is significant because the fundamental goal of data visualization is to demonstrate how variables change over time.

Time series data visualization examples

 

Here is a fast list of the types of visuals that fall under the category of time series visuals. You may learn more about the various uses of time series data visualization in the sections that follow.

 

• Line chart

• Bar chart

• Area chart

• Sphere chart

 

Effective data visualization is created by the collision of communication, data science, and design. Data visualization made the right key insights into complex data sets meaningful and natural.

 

To create an effective data visualization, you need to start with clean data that is well-sourced and complete. After the data is ready to be visualized, you need to select the correct chart.

 

After you've decided on a chart type, you need to design and customize the visualization to your liking. Simplicity is essential – you don't want to add elements that distract from the data.

 

 

The meaning of data visualization is simple: it helps people see, work with, and better understand data. Whether simple or complex, the proper visualization can get everyone on the same page, regardless of their level of expertise.

 

It's hard to imagine a professional industry that wouldn't benefit from more understandable data. Every STEM field benefits from understanding data—and so do areas in government, finance, marketing, history, consumer goods, services, education, sports, and so on.

 

While we will always wax poetic about data visualization (you are on Tableau, after all), there are undeniable practical, real-life applications. And because visualization is so prolific, it's also one of the most beneficial professional skills to develop. The better you can communicate your points visually, whether on a dashboard or a slider, the better you can use that information. The concept of citizen data scientists is on the rise. Skill sets are changing to fit a data-driven world. It is increasingly valuable for professionals to use data to make decisions and use visuals to tell stories about when data informs the who, what, when, where, and how.

While traditional education usually draws a clear line between creative storytelling and technical analysis, the modern professional world also values those who can cross the line: data visualization is correct in the middle of research and visual storytelling.

 

 

When you think of data visualization, your first thought immediately goes to simple bar graphs or pie charts. While they can be an integral part of data visualization and a common starting point for many data graphics, the proper visualization must be paired with the correct information set. Simple graphs are only the tip of the iceberg. There are several visualization methods to present data effectively and excitingly.

 

 

Chart: Information presented in tabular graphic form with data displayed along two axes. It can be in the form of a graph, diagram, or map. Find out more.

 

Table: A set of numbers displayed in rows and columns. Find out more.

 

Graph: A graph is a representation of specific variables in relation to one another using points, lines, segments, curves, or regions, typically along two axes at right angles.

 

Geospatial: A visualization that shows the connections between various bits of data and certain locations on a map using various shapes and colors. Discover more.

 

Infographic: An infographic is a representation of data that combines words and images. employs charts or diagrams frequently.

 

Dashboards: A collection of visuals and data that are all displayed in one location to aid in the presentation and interpretation of data. Discover more.

 

 

There may be no drawbacks to doing something as straightforward as presenting data graphically. However, when used in the inappropriate data visualization style, data might occasionally be presented or understood inaccurately. It's best to consider both the benefits and drawbacks while creating a data visualization.

 

 

Colors and patterns catch our attention. We can swiftly differentiate squares from circles and red from blue. Everything in our culture is visual, from art and advertising to television and movies. Another sort of visual art that captivates us and keeps our attention on the message is data visualization. We can immediately see trends and outliers when we look at a chart.

 

• Easy information sharing.

• Explore opportunities interactively.

• Visualize patterns and relationships.

 

While there are many edges, some drawbacks may seem less obvious. For example, it is easy to make an inaccurate assumption when viewing a visualization with many different data points. Or sometimes, the visualization is simply poorly designed so that it is biased or confusing.

 

• Some other disadvantages include:

• Biased or inaccurate information.

• Correlation does not always mean causation.

• Important messages can get lost in translation.

 

 

Data visualization is beautiful. Not only does it help you visualize complex data more efficiently, but it also lets you quickly identify patterns and relationships you never knew existed.