Data Visualization: Definition, Benefits, and Examples

Written by Coursera • Updated on

Data visualization helps data professionals tell a story with data. Here’s a definitive guide to data visualization.

[Featured Image]:  Data visualization analysts presenting and information with the team.

A company such as Google will track the number of searches made in each country, and what types of keyword searches are being made, so that companies, government agencies, and other organizations can stay attuned to trends and patterns of the general public.

This requires the company to collect “big data” and synthesize it into information. Google can use data visualization to portray significant insights—like a heat map to illustrate regions where individuals search for mental health assistance. Data visualization helps big data professionals tell a story with data, turning spreadsheets of numbers into stunning graphs and charts.

In this article, you’ll learn all about data visualization, including its definition, benefits, examples, types, and tools.

What is data visualization?

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations enable data professionals to easily understand any patterns, trends, or outliers in a data set.

Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible manner. For example, the health agency in a government (in the US, that would be the CDC) might provide a chart of populations with the highest cases of COVID-19 or a map of a country colored according to vaccinated regions.

The purpose of data visualization is to help drive informed decision-making and to add colorful meaning to an otherwise bland database.

Benefits of data visualization

Data visualization can be used in many contexts in nearly every field, like public policy, finance, marketing, retail, education, sports, history, and more. Here are the benefits of data visualization:

  • Storytelling: People are drawn to colors and patterns in clothing, arts and culture, architecture, and more. Data is no different—colors and patterns allow us to visualize the story within the data.

  • Accessibility: Information is shared in an accessible, easy-to-understand manner for a variety of audiences.

  • Visualize relationships: It’s easier to spot the relationships and patterns within a data set when the information is presented in a graph or chart.

  • Exploration: More accessible data means more opportunities to explore, collaborate, and inform actionable decisions.

Tools for visualizing data

There are plenty of data visualization tools out there to suit your needs. Before committing to one, consider researching whether you need an open source site or simply to create a graph using Excel or Google Charts. The following are common data visualization tools that could suit your needs. 

  • Tableau

  • Google Charts

  • Dundas BI

  • Power BI

  • JupyteR

  • Infogram

  • ChartBlocks

  • D3.js

  • FusionCharts

  • Grafana


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Data visualization examples

Using data visualization tools, different types of charts and graphs can be created to illustrate important data. These are a few examples of data visualization in the real world:

  • Data science: Data scientists and researchers have access to libraries using programming languages or tools such as Python or R, which they use to understand and identify patterns in data sets. Tools help these data professionals work more efficiently by coding research with colors, plots, lines, and shapes.

  • Marketing: Tracking data such as web traffic and social media analytics can help marketers analyze how customers find their products, and whether they are an early adopter or more of a laggard buyer. Charts and graphs can synthesize data for marketers and stakeholders to better understand these trends. 

  • Finance: Investors and advisors focused on buying and selling stocks, bonds, dividends and other commodities will analyze the movement of prices over time to determine which are worth purchasing for short- or long-term periods. Line graphs help financial analysts visualize this data, toggling between months, years, and even decades.

  • Health policy: Policy makers can use choropleth maps, which are divided by geographical area (nations, states, continents) by colors. They can, for example, use these maps to demonstrate the mortality rates of cancer or ebola in different parts of the world. 

Get started with a free tool

No matter the field, using visual representations to illustrate data can be immensely powerful. Tableau has a free public tool that anyone can use to create stunning visualizations for a school project, non-profit, or small business. 


Types of data visualization

Visualizing data can be as simple as a bar graph or scatter plot but become powerful when analyzing, for example, the median age of the United States Congress vis-a-vis the median age of Americans. Here are some common types of data visualizations:

  • Table: A table is data displayed in rows and columns, which can be easily created in a word document or Excel spreadsheet.

  • Chart or graph: Information is presented in tabular form with data displayed along an x and y axis, usually with bars, points, or lines, to represent data in comparison. An infographic is a special type of chart that combines visuals and words to illustrate the data.

    • Gantt chart: A Gantt chart is a bar chart that portrays a timeline and tasks specifically used in project management.

    • Pie chart: A pie chart divides data into percentages featured in “slices” of a pie, all adding up to 100%. 

  • Geospatial visualization: Data is depicted in map form with shapes and colors that illustrate the relationship between specific locations, such as a choropleth or heat map.

  • Dashboard: Data and visualizations are displayed, usually for business purposes, to help analysts understand and present data.

Jobs that use data visualization

From marketing to data analytics, data visualization is a skill that can be beneficial to many industries. Building your skills in data visualization can help in the following jobs:

  • Data visualization analyst: As a data visualization analyst (or specialist), you’d be responsible for creating and editing visual content such as maps, charts, and infographics from large data sets. 

  • Data visualization engineer: Data visualization engineers and developers are experts in both maneuvering data with SQL, as well as assisting product teams in creating user-friendly dashboards that enable storytelling.

  • Data analyst: A data analyst collects, cleans, and interprets data sets to answer questions or solve business problems.

Data is everywhere. In creative roles such as graphic designer, content strategist, or social media specialist, data visualization expertise can help you solve challenging problems by demonstrating data analysis skills and business acumen. You could create dashboards to track analytics as an email marketer or make infographics as a communications designer.

On the flip side, data professionals can benefit from data visualization skills to tell more impactful stories through data. Visual storytelling is a powerful tool for transforming data insights into actionable business decisions.

Read more: 5 Data Visualization Jobs (+ Ways to Build Your Skills Now)

Dive into data visualization

Learn the basics of data visualization with the University of California Davis’ Data Visualization with Tableau Specialization. You’ll leverage Tableau’s library of resources to learn best practices for data visualization and storytelling, learning from real-world and journalistic examples. Tableau is one of the most respected and accessible data visualization tools. 

To learn more about data visualization using Excel and Cognos Analytics, take a look at IBM’s Data Analysis and Visualization Foundations Specialization.



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Written by Coursera • Updated on

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