data visualization

Data Visualization examples

Data is no longer something that just data specialists need to think about. Data now drives all good business decisions.

Data visualization makes it easier to understand and interpret large amounts of text and data by transforming it into a visual form. In this article, let’s see some data visualization examples and steps to build them.

It’s important to create visualizations that help the users. Also, make sure that your visuals don’t become misleading. Considering these, here are some steps that you should follow:

Data visualization examples

Here are some data visualization examples. You will choose one of them depending on what you want to communicate to your audience.

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

Table: A set of figures displayed in rows and columns.

Graph: A diagram of points, lines, segments, curves, or areas that represent certain variables in comparison to each other, usually along two axes at a right angle.

This example of a graph is a representation of a wind speed histogram.

Geospatial: A visualization that shows data in map form using different shapes and colors to show the relationship between pieces of data and specific locations.

This example of Geospatial displays the map of the Airbnb listings in the city of Austin, USA.

Infographic: A combination of visuals and words that represent data. It usually uses charts or diagrams.

Dashboards: A collection of visualizations and data displayed in one place to help analyze and present data.

This example of a Dashboard is built on Python codes, with the Plotly library. It shows the Process Control and Exception Reporting.

Tools for Data Visualization

I started exploring Data Visualization by using Python libraries. In fact, it was an entry point to the Data Analysis gateway. Another popular programming language that offers a similar path is R.

Data visualization tools vary from no-code business intelligence tools like Power BI and Tableau to online visualization platforms like Google Charts.

When deciding on a data visualization tool, you should consider the following factors:

  • Easy to learn. Simpler data visualization tools are better suited for non-technical users, but they come with more constraints and limitations.
  • Flexibility. If you want complete control over every little aspect of your visualizations, you should go for tools with wide flexibility.
  • Price. Price is an important factor to consider when choosing a data visualization tool. Depending on your needs and budget, some tools will function better than others.

Creating data visualizations is about more than making data easier to understand. It’s about telling a story and making sure that everyone in your team or organization has a role to play.
Happy data visualizing 📊