The first commandment of data visualization is to define or identify the purpose—i.e., what to visualize or show. Purpose comes from knowing your stakeholders and their objectives.

For decades, visualization was the final stop on the data journey. It was optional—"good to have" on top of data analytics. Today, that model doesn't work anymore. In the world of AI business ...

Box and whisker charts provide a visual summary of one or more sets of data highlighting their distribution and spread. They are useful for identifying outliers and understanding the dispersion and ...

Here is where dynamic visualization in Excel comes in clutch. In this post, we will go over the techniques to transform your ordinary spreadsheets into powerful tools for data discovery.

Finally, make sure that your data visualizations aren’t just pandering to the muse. Of course, appearance matters — but it’s not the only thing that matters.

The future of data visualization is increasingly intertwined with AI-powered automation, enabling systems to detect patterns, generate real-time reports, and automatically suggest key insights.

Design with empathy Background: Commonly used forms of data visualization, such as bar charts, line charts, and pie charts, while informative, can abstract content by collapsing the people represented ...

Data visualizations are some of the most powerful tools in a climate science communicator’s playbook. The most famous have ...

As the data can change, it is a best practice to create test automations on data visualizations that run in continuous integration and continuous delivery (CI/CD) pipelines but can also run as ...