Level Up Your Data Design Strategy
Updated: Jun 2, 2021
People often think of data analysis as a left-brain activity – data living as numbers in orderly black and white columns. But that is totally wrong! The concept of data visualization, or creating visual representations of data for easy consumption, is a constantly growing, billion-dollar industry. With the help of increasing interest in business intelligence software, the visual appeal of data presentation plays a larger role than ever before. So, how can data become designed instead of simply presented?
Why consider visual aspects of your data?
Data visualizations revolve around preattentive attributes. These are things that humans understand intuitively about any visual object, such as which bar in a bar chart is longest or shortest in length. These attributes also affect our understanding of color – green is usually regarded as good while red indicates something negative or unwanted. Look at the chart below showing number of orders categorized by weekday:
Does any insight immediately become clear? Probably not. The use of color in this example does not enhance understanding of the data’s meaning. Now, compare the first chart to the second one below:
Instantly it becomes obvious that the number of orders on weekends is noticeably lower than on weekdays. This is the effect of color on our human minds using preattentive attributes.
Mastering the design of data visualizations requires understanding these preattentive attributes and using them to your advantage. Organizations such as Storytelling with Data offer valuable resources for learning more about this topic.
Key Principles to Consider in Design
By considering the principles listed in this post while creating data visualizations, the design process will not only be more straightforward, but the outcome will almost certainly provide an upgraded user experience.
In dashboard design, less is more. Often, people creating data visualizations include redundant labelling and headers to make their insights clear when they are actually doing the exact opposite! When too much additional information is included in a visualization, even if it is repetitive, the user spends preci