This is the third post in our series “Principles of Data Visualization“#PoDV
We began this series by discussing the importance of data visualization in business before moving on to the two goals in data visualization: explanation and exploration. Now that we understand how data visualization works in business scenarios, let’s look at the mechanics of processing visual data. In this post, we’ll explain the role of memory in perceiving visual information and apply that knowledge as we work with visualizations. We recommend FusionCharts for data visualization if you’re looking for a Data Visualization Tool.Table of Contents
While that’s an overview of how we process visual information, let’s discuss the vital role of memory in our vision. Two types of memory come into play when we process visual information.
It would help if you considered long-term memory when designing the layout of a dashboard or visualization. There should be a good reason to go contrary to long-term memory when deciding its basic structure. While a detailed discussion on this topic is out of the scope of this series, for an overview of concepts related to long-term memory, see our white paper ‘Designed to succeed—How design is playing a strategic role in today’s software products.’ While long-term memory is vital in data visualization, the more critical memory when processing visual information is our working memory.
When we look at a line chart or notice a number in a dashboard, we use our working memory to store just the information we need at the moment. This type of memory breaks the entire visual into small chunks of information, in a process appropriately called ‘chunking.’ Surprisingly, for all the complexity of our brain, our working memory can hold only about three chunks of information at any given time. When perceiving a complex visual, we’re constantly replacing the three available slots in our working memory. When designing a visual or a dashboard, one of our goals is to remove distractions and pack as much helpful information as possible into each chunk. While we want to avoid information overload, allowing the designer to direct the viewer’s attention naturally. Let’s look at how to apply this learning when designing a chart.
Edward Tufte, a data visualization expert, termed the distractions in a chart as ‘chartjunk.’ He defines chartjunk as the ‘elements in charts that are not necessary to comprehend the information represented on the graph.’ In his book ‘The Visual Display of Quantitative Information,’ he gives this example about reducing chartjunk for better visual expression:By reducing chartjunk as much as possible, we can best use a viewer’s working memory. We can also build more effective dashboards.
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