The gamut of data visualization tools that are available now have made it easier for us to consume intelligence from a mass of information. The traditional visualization tools we built to showcase data in “defined” format. Most of the data visualization vendors fail to incorporate the biology of human eye into their visualization techniques which resulted in visualizations that were great “eye-candy” but poor in human comprehension of data.
It would be too harsh to pass on the entire blame for this to the data visualization vendors. Data visualization developers also need to keep in mind – how the visualization would be consumed by the users? Is it easy to understand?
A good indicator for this is the 5 second test. If a visualization can communicate the right information within 5 seconds – we call it a great visualization.
How does a human eye perceive a data visualization?
Human brain is spectacular. It is pivotal in the way we consume, process and infer information. A good understanding of this process enables developers to build user friendly dashboards.
To understand this – lets understand the linkage between thinking & seeing.
The first step in data consumption is – SEEING.
When we see a visualization, it is received by the visual cortex part of the brain. All the physical attributes of vision – length, shape, size, color, position etc. are captured by the visual cortex and sent to cerebral cortex for processing.
The second step in data consumption is – THINKING
This is where the data elements captured by the visual cortex is processed by the cerebral cortex to make inferences. It is all too easy to utilize color combinations that contribute to user confusion or to add distracting animations or graphics that reduce clarity while increasing the amount of time required to make sense of the data being presented.
A pre-attentive visual property is one which is processed in spatial memory without our conscious action. It takes less than 500 milliseconds for the eye and the brain to process a pre-attentive property of any image. It means that these properties can be harnessed to make it easier for a user to understand what is presented through the design and save them from consciously processing all the data presented in short-term memory which requires more effort.
How Do Humans Like Their Data – Some examples?
- Time: on an x-axis
- Location: on a map
- Comparing values: bar chart
- Exploring relationships: scatter plot
- Relative proportions: treemap
Keeping these pre-attentive attributes in mind, developers can easily draw attention to the most important information that they want to convey.
Remember, too much information is as bad as having too less information.