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May 24, 2022
Data Visualization

How to use visual design to improve organization and clarity of information

You must be able to effectively tell your data story

If you’re a data scientist or designer, odds are high that you ask yourself “How do I show the results of my work efficiently and clearly – how do I get my point across?” After all, if you can’t effectively tell your data story to your audience, it won’t matter how great your code is or how innovative you are in your approach.

Once you understand the ways in which perception and psychology influence data visualization, and when you have a grasp of even the most basic of data visualization techniques (of which can be found in the Complete Guide to Better Data Visualization) you can help your viewers absorb more information from data visualization faster and with less effort. This minimizes conscious processing (as well as subconscious pre-attentive processing) and memory load. 

In order for your data story to be told effectively, it’s important to have a knowledge of these concepts to optimize the clarity, conciseness, and impact of your data visualization.

“Visual designers are trained in using techniques and strategies to move a viewer’s eye across a design in intentional ways. Use of even the most basic visual design techniques can help data practitioners—especially those who aren’t designers by trade—explore visualization techniques that can be used to add a strong sense of organization, unity, hierarchy, and clarity.”Courtney Ulrich Smith, Director of Design Strategy, RevUnit

How understanding basic design principles can level up your data viz skills:

  • Helps to reinforce narratives or perceptions of data by using specific color schemes
  • Creates a framework for techniques to move a viewer’s eye across a visual display
  • Informs how typography can influence visual hierarchy and clarity of information
  • Provides a window into how our perceptions are influenced by shape and color


Are you familiar with the foundational design principles?

The visualization of data should move the viewer’s eye across the display in a specific way. This is known as compositional flow; it determines how the eye is led, where it looks first, where it looks next, and where and how long it pauses. The eight principles of design greatly influence this flow, so we encourage every data practitioner to have a basic understanding of them.

These principles play a part in all areas of your display; shape, color, typography, relation. 

  • Alignment      
  • Hierarchy     
  • Contrast      
  • Repetition      
  • Proximity      
  • Balance      
  • Color      
  • Space


The four design principles that seem to have the most impact when it comes to data visualization

The C.R.A.P Method is a straightforward, easy way to approach layout and visual design implementation, and can help take your designs to the next level. 

Contrast

Contrast is an efficient way to differentiate what's important from what's not, and to aid in creating hierarchy to help a viewer find the information they’re looking for.

Repetition

Repetition is basically the fancy visual design code word for the Gestalt principle of "similarity". Repetition allows you to group like elements, assigning attributes to each as needed, then re-using those elements in your visualizations. 

Alignment

Alignment is the placement of visual elements so they line up in a composition; it’s used to create visual hierarchy, to organize elements, to group elements, to create balance, to create structure, to create connections between elements, to create a sharp and clear outcome. 

Proximity

Proximity refers to the location of various elements in relationship to one another. When items are organized close together, and those items are separated from other items, they are perceived to be related by proximity. 


Do you know the principles of color theory and its role in visualization?

As a data practitioner seeking to create effective visualization, you should familiarize yourself with the principles of color theory and what effects they can have in your visual displays — especially if you aren’t classically trained in design. 

There are a few elements of color that you should care about most when it comes to using data visualization to effectively communicate to your company or customers: 

Color Anatomy

Color Wheel

Color Harmony


Download the Complete Guide to Better Data Visualization to read more about each of these elements and to see examples of recommended color schemes. 

Understanding these concepts will help you choose the right color scheme for your visual display. Color schemes can be repeated to emphasize similarity; they can be contrasted to differentiate what’s important from what’s not, which also creates visual hierarchy. These design principles should be incorporated often into visual displays to reinforce intended narratives. 


Do you know the typography principles that create hierarchy?

Typography is the study and practice of styling and arranging type. It goes beyond just knowing a few fonts. It’s about understanding font types, how to manipulate letters and spacing, and how to use letters to create hierarchy and make content easy to consume. 

The color, size, alignment, and proximity of type all impact the visual hierarchy of your data, and, by extension, others’ perceptions of it. Well organized typographical elements create a strong visual hierarchy with a sharp and clear outcome.

Bottom line: In 2022, the need for good data visualization skills is at its most critical. It’s important for every leader and data practitioner to recognize and capitalize on the ways in which good visualization practices can be used for improved cohesion and clarity of the information they have at hand.

Ready to get started? Check out our Complete Guide to Better Data Visualization and download our “3 Tips for Better Data Storytelling with Your BI Tools” cheat sheet.

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WRITTEN BY
RevUnit
RevUnit is a data technology studio focused on building data strategies, products, and visualizations to get operational insights into the hands of employees, faster. We’ve tackled data initiatives — from custom analytics software to data innovation labs — for companies like Walmart, Tyson, and Hyatt.

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