This article will explore how elements of visual design can be incorporated into data visualizations to present data more clearly and effectively.
What is Visual Design?
Visual design is a creative field devoted to improving a product’s usability or aesthetic appeal. Those who work with visual design incorporate design techniques and tools, such as typography, image selection, layout, color, and white space, to improve on an existing product. Visual Designers deliberate on where elements should be placed within an interface to provide the best user experience and to ultimately generate more conversation on the topic.
One core element of visual design is anticipating where audience members will look or click on images. This knowledge offers insights into how designs are perceived and which part of the design holds the most pertinent content. Many of the techniques used in creating effective visual designs have applications for Data Analysts creating data visualizations.
What is Data Visualization?
One of the most useful tools available for presenting complicated material in an accessible way is data visualization. This rapidly evolving field is focused on using visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data aren’t just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve a desired outcome.
There are many kinds of data visualizations, each of which serves a specific professional purpose. Some of the most popular techniques for conveying information are:
- Scatter plots
- Infographics
- Histograms
- Waterfall charts
- Area charts
- Maps
- Pie charts
- Bar charts
- Box-and-whisker plots
- Heat maps
Because we live in an increasingly visual culture, those who know how to present information in visually engaging stories have the power not only to help make sense of past events but to offer predictions for the future as well.
Effective data visualization has many benefits for both internal and external stakeholders. It:
- Allows viewers to spot areas that require improvement.
- Provides a way to pinpoint variables that influence customer behavior.
- Improves on product placement.
- Identifies frequency patterns, such as how often a product is purchased in a given area.
- Anticipates sales volumes.
- Analyzes risks and addresses issues before they grow into problems.
- Examines relationships between productivity and oversight.
- Helps users implement a roadmap for future actions.
Tips for Using Visual Design for Effective Data Visualization
Because the human brain is able to process visual content much faster than text, presenting data in a manner that the audience can “see” it is a powerful tool. However, simply piecing together charts to share is not the same as creating effective data visualizations. Sub-par data designs can negatively impact not only the audience’s understanding of the information you wish to convey, but can hurt the brand itself. Faux-pas such as mislabeled data, crowded or confusing visualizations, and skewed perceptions are just a few of the common mistakes that complicate visualizations.
The following are a few tips to help Data Analysts more effectively incorporate elements of visual design into their data visualizations:
- Pick the best chart type (such as histogram or pie chart) to tell the story.
- Eliminate any extraneous information. Any data point that is not directly supporting the story you wish to tell, such as superfluous illustrations and ornamentation, should be deleted from the visualization.
- Be cognizant of where visual components are placed within a visualization. For example, when you want the reader to compare points on two stacked bar charts, if they are too far apart from one another in the visualization it will be hard for them to do so.
- Avoid over-explaining information. When a fact is mentioned in the copy, it’s not necessary to repeat it in the subhead or chart.
- Create designs that facilitate comprehension. After you finish a draft of a data visualization it’s important to step back and objectively evaluate it. At this point, missing elements like trend lines can be added to enhance clarity. Similarly, you may realize that some elements should be removed to help with audience comprehension.
- Show rather than tell. This is a basic rule for good storytelling regardless of field or genre, but has direct application in the realm of data visualization. No audience member wants to be confronted with a boring block of text when an image could do the same work.
- Make visualizations inclusive. Color is a powerful tool to represent various types of information visually, and can play a crucial role in driving user decisions. Select colors carefully and deliberately to show contrast, highlight important points, and visually direct the eye.
- Avoid patterns in data visualizations. Although it can be tempting to add elements such as polka dots or stripes, they can distract from the information you are presenting and the patterns or trends emerging in the data.
- Simple is generally better when creating headers. It’s preferable to provide the audience with a clear, brief, and direct header for charts and graphs, rather than one that is wordy or trying to be clever.
- Order data intuitively, consistently, and evenly. For example, the ordering of items in the legend should be the same as those in the chart. There should be a logical hierarchy to the way data is ordered, such as sequentially or alphabetically. In addition, it’s important to work with natural increments on the axes, such as 0, 5,10, rather than uneven increments.
As the above list indicates, visual design plays an important role in all aspects of the data visualization process, from selecting chart type to picking color, headers, and overall layout. Data visualization isn’t just a science, it’s also an art that combines visual design and storytelling to bring data to life. Those with visual design skills have the power to transform data into actionable insights.
Hands-On Data Visualization Classes
Are you interested in learning more about how to create stunning and helpful data visualizations? If so, Noble Desktop offers more than 80 live online data visualization classes, which range in price from $229 to $12,995. Noble also offers a Tableau bootcamp specifically designed for students who want to create their own interactive data visualizations. The bootcamp experience provides students with small class sizes, expert instructors, and even the option of a free retake to brush up on core class components. Tableau bootcamp spans twelve hours, and is available in-person in New York City, as well as in the live online format.
Those who are interested in finding nearby Tableau classes can use Noble’s Tableau Classes Near Me tool. This handy tool provides an easy way to locate and browse more than three dozen of the best Tableau classes currently offered in the in-person and live online formats so that all interested learners can find the course that works best for them. In addition, for those searching for a data visualization class nearby, Noble’s Data Visualization Classes Near Me tool makes it easy to locate and learn more about over 200 courses currently offered in the in-person and live online formats. Class lengths vary from three hours to five months and cost from $119 to $12,995.