Whether you are aware of it or not, you are constantly recognizing and processing patterns in your everyday life. Think about when you go to dinner at a sit-down restaurant that you’ve never been to before. Even though you’re trying the restaurant for the first time, you will have some expectation about the order of events, which usually looks like this:
You walk in and are greeted by a host or hostess who shows you to a table
The waiter comes by to introduce themselves and takes your drink order
After returning with your drinks, the waiter takes your dinner order
Throughout the meal, the waiter may stop by every once in a while to make sure you’re doing okay and refill your drinks
After the meal, the waiter will make a bad joke about "saving room for dessert"
After declining, they will either produce your bill from their pocket or run over to the register to get it for you
You pay and go on your way
This is one of many examples of a psychological schema (not to be confused with database schema) in your everyday life. These patterns help society align diverse audiences and help us process varying situations very efficiently. These schemas are so powerful and ingrained that a disruption to the pattern can be confusing and challenging to overcome. As one extreme example, imagine showing up to the restaurant from above and having the waiter bring you a check before you’ve sat down.
The restaurant pattern goes in an intuitive order so it is unlikely to vary much, but you also create schemas that are personal to you based on your own life experiences and worldview. These expectations help you avoid reinventing the wheel because you’ve experienced the same or similar situation before and know how to handle it.
Schemas play an important role in data visualization because they have the ability to make or break the two biggest benefits of visualizing data: reducing time to insight and improving the accuracy of insights. Tap into your audience’s schemas and you improve their experience; disrupt their schemas and you run the risk of leading your audience in the wrong direction.
This post shares three ways to leverage schemas to improve your data visualization.
Schema 1: Spatial context
Maps help us process data because in addition to the data point, they provide spatial context that help our analyses. Consider the following bar chart showing the lowest cost per section to attend Super Bowl 50:
This a good data visualization within best practices, and there are definitely insights to be found in this chart. However, adding spatial context immediately helps the analysis make sense, even if you are not familiar with the stadium where this game was played:
I can use the schema I’ve constructed in my mind from my experiences buying tickets to many sporting events over my lifetime to know that the lower and closer to midfield you are, the more expensive the ticket will be. This will help reduce my time to insight because it’s much faster for me to determine if the numbers on the bar chart make sense to me intuitively or if there is a disruption to my schema, which in this case would also lead to insight (i.e. if lower bowl tickets are going for less money than the upper deck).