Ai Ching

CEO | Piktochart

We are living in the age of big data. We can now monitor anything and establish connections (or just correlations) between events. With so much data, it is imperative to find the best way to make the data tell a story. And communicate the right information to people. Hence, enter data visualization.

Which is why we put together this interactive site made up of 85 eye-catching data visualization examples, to inspire and motivate you in your next project. Click the below image to check it out!

Visual data matters, simply because:

  • Conveys: Data tells story. It persuades people about something.
  • Collaborates: Data support your points. It presents an interesting or thought provoking argument.
  • Reasoning: Data provides insights. It helps you make decisions.

So why do we need to visualize data? To answer that, let us first take a look at the principles of data visualization—explanation and exploration—with examples.

Two major principles of data visualization:

1. Explain data to solve specific problems

Some datasets visualized answer your problem. Here’s a typical example in a business environment.
Say you have new magical delicious coffee beans to sell. So you asked, “Which country should I sell beans to?”

Image credit: Thomson Reuters

From the bar chart above, it is obvious which country you should target your business on. Perhaps, to further coagulate your decisions, data on Finland’s brew preference can be collected for further analysis.

2. Explore large data sets for better understanding

The other principle of data visualization is exploration. Sometimes it is difficult to discern a situation when you have huge data.

As asserted by Jeffrey Veen, “Data can change things if stories are told correctly.”

Here’s an example of visualized data story done right.

The above example is a short presentation done by Jeffrey Venn. He talked about how big data can be visualized to save the world (in a way). He takes us back to the 1800s to look at a cholera outbreak. Most visualization mashed up the map and discerns that it is an airborne disease. However, John Snow took a closer look at the data. He realized that the problem could have started from a water irrigation system. Very likely that it stemmed from a faulty water pump.

This is a proven case of how data can be visualized to enhance our understanding of a situation (or problem).

Tips to visualize data:

  • Always leave the interpretation of data to your users.
  • Provide accurate data depiction.
  • Give your users the right tool to help them interpret data.
  • Let go of the control. Allow your readers to find their own story.

This post was inspired by the slides at Stanford Edu and Jeffrey Veen’s presentation.