On January 28, 1986, the Challenger launched into space. Over a minute later, the entire shuttle disintegrated, killing all crew members aboard. Organizational theory classes have cited this as an example of how ineffective organizational structure led to the Challenger tragedy. Edward Tufte, a well-known statistician and data visualization expert presents the tragedy in a different perspective. He argues that not having the data presented in an easily readable format led to the tragedy as well.
O-rings, which are used to seal the bottom of rocket boosters, caused the shuttle to break apart shortly after taking off. They were known to fail at cold temperatures, and the temperature was 36 degrees Fahrenheit. Engineers who worked on the project realized that the O-rings would most likely fail in the cold weather for the launch the next morning. They went to their superiors pleading their case about how the cold temperatures for the launch would cause the O-rings to fail. Their superiors decided to move forward with the launch.
Before the decision, the engineers presented their data with charts of the rockets to try to illustrate the damage cold temperatures would have on the O-rings. (The charts can be seen here.) The scientists used charts displaying outlines of rockets with temperatures and organized the rockets by launch date. They labeled the temperatures of O-ring damage on its side, making the entire chart difficult to read. Tufte argues that they should have organized the charts by temperature and improved the chart labels to better illustrate how temperature affects O-ring reliability.
Providing a better analysis and visualization of the data could have helped improve the decision-making process and potentially built a stronger case for the engineers about the effect of cold weather on O-ring functionality.
No one likes to look at a large amount of data for instance to figure out what patterns could be hidden in the data. Data visualization provides a way to communicate large amounts of data in a user-friendly visual. Some of the ways data visualization could help at work are identifying areas for cost improvements, analyzing sales-trend data over time or managing workloads.
Data visualization ultimately helps communicate and tell a story. It can help build a persuasive case or help answer a question. With the amounts of data being generated, data visualization is one technique to harness.
The original source for the story can be found here.
Elaine Nghiem is part of the GovLoop Featured Contributor program, where we feature articles by government voices from all across the country (and world!). To see more Featured Contributor posts, click here.
This is one of the best posts I’ve ready drawing on the importance of data visualization. That topic can seem so mundane to some, but this is a really great example of how it can even mean life or death. Awesome post, Elaine!