Avoid Analytics Mistakes by Being Aware of Misinformation Visualization

Due to the increased capabilities of technology, data visualization has become more accessible in recent years. Many software programs and other tools help people “see” data quickly, but there was a time when it used to take much longer.

Although making sense of data without huge paragraphs of text or daunting spreadsheets is extremely advantageous to some people — depending on how they prefer to digest data — it’s also important to understand misinformation visualization. Data analysts have a responsibility to prevent it so that people correctly interpret analytics.

One of the reasons data visualization presents such valuable opportunities is that people can process pictorial data much quicker than text alone, according to some statistics. Specifically, one study found humans could process images in as few as 13 milliseconds. That means data interpretation can theoretically begin before conscious thought does, letting people immediately start understanding what they see.

For decades, people have believed in different learning styles and that when individuals receive content most suitable to their style, they’ll be most likely to comprehend it. The concept is still pervasive in society, and most people still think it’s valid even though scientists have had difficulty proving that learning styles exist.

So data specialists who think, “I need to present my data visually because my audience is full of individuals who learn best with pictures” may be relying on outdated information. Instead of focusing on what viewers might like to see, it could work better to think about whether pictures would make certain data more educational than they would be through text alone.

A positive quality of data visualization is that it can illuminate statistics that aren’t easy to identify in other, picture-less formats. As a result, it often highlights trends and patterns not apparent when people view data in other ways.

In the same way a filmmaker has a certain goal in mind when shooting a documentary, data analysts usually have objectives too. It’s crucial to remember that a person can present visual data in numerous formats. An individual preparing to show a chart to an auditorium full of people may experiment with various graphical representations.

In a positive scenario, this approach allows data analysts to figure out the most effective ways to get people to the heart of the story told by the statistics. However, it’s also essential to make sure the data is not presented in a way that leads to misinformation visualization.

When people see information explained through data visualization, they must remember to ask themselves whether what’s shown is — purposefully or unintentionally — only showing part of the story.