Key design principles for efficacy in Data Visualization

The role of data visualization in communicating the complex insights hidden inside data is vital. This is becoming more and more important since the audience for data visualizations is also expanding along with the size of data.
Key design principles for efficacy in Data Visualization
Key design principles for efficacy in Data Visualization

The role of data visualization in communicating the complex insights hidden inside data is vital. This is becoming more and more important since the audience for data visualizations is also expanding along with the size of data. Data visualizations are now consumed by people from all sorts of professional backgrounds. For the same reason, the ease of consumption is now a hot topic. While data scientists and analysts have an eye for digging out the key insights from even complex visualizations, a top business stakeholder or an average person might not be able to do the same.

And this is what makes effective data visualization the need of the hour. Communicating the data effectively is an art. However, many data scientists lag behind when it comes to the design and aesthetic aspects of visualizing data.

The r/dataisbeautiful subreddit on reddit is a great place to find some curated data visualizations from around the web. Here are some of the key design principles for creating beautiful and effective data visualizations for everyone.

1. Balance the design

A balanced design is one with the visual elements like shape, color, negative space and texture equally distributed across the plot. This does not necessarily mean the design should be an exact copy of the other. You can bring out an asymmetrical balance by offsetting bigger graphs and charts with smaller elements.

There are three different types of balances in design:

  1. Symmetrical – Each side of the visual is the same as the other.
  2. Asymmetrical – Both sides are different but still have a similar visual weight.
  3. Radial – Elements are placed around a central object which acts as an anchor.

You will have to figure out which type of balance works the best for your data visualization and apply that.

2. Emphasize the key areas

The user’s attention should be drawn to the right data points by carefully choosing the size, colors, contrast and negative space. The goal of the visualization is to make sure that the important data doesn’t go unnoticed and emphasizing it helps. Since the attention of a user first falls in the top-left corner of a plot first, you should place the important data points there.

3. Illustrating movement

Movement directs the user’s attention in a certain direction, just like emphasis. Your visual elements should mimic movement in an “F” pattern which is how people read. Starting from top left to right, and gradually down the page. You could also illustrate movement across the page by using complimentary colors that can catch the viewer’s gaze and take it across the page. This principle is more applicable to static visualizations. If your visualization tool is capable of animation and interactive designs, the movement aspect should already be covered.

4. Smart use of patterns

Repeated design elements form a pattern. When it comes to visualizing your data, patterns make for a great way to display similar type of information spread across the page as one. If the data on the page is too much for emphasizing, establishing a pattern by using similar colors, chart types and elements is the way to go. Patterns also make it easier to communicate an anomaly since any disruption in the pattern will naturally draw the viewer’s attention and curiosity. Using patterns is one of the simplest and most effective design principle when it comes to data visualization.

5. Proportion

If you are going to draw the picture of a bird on a tree, the tree will be significantly bigger compared to the bird. In a data visualization, the proportion is made up of the size of each element in the page. Proportions in data visualization can indicate the weight of different data sets and the relationship between their values. If you need to emphasize the importance of a certain data point, all you have to do is to make it bigger than the rest. In addition to this, you should ensure that the chart reflects the interrelationship of various numbers as accurately as possible. For example, if a slice in a pie chart is marked 36%, it should actually use 36% of the area inside the chart.

6. Proper rhythm

Rhythm is a rather vague design principle which is closely associated with movement. A design is said to have a balanced rhythm when the design elements together create a pleasing movement to the eye. If the design elements like shapes, colors or proportions together create a “choppiness”, you might want to rearrange them so as to facilitate smooth eye movement across the data.

7. Variety

Variety is an important factor that keeps viewers engaged and interested in your data. It’s all about finding ways to visualize your data using different and interesting design elements so as to avoid repetition. The end result will be a data visualization which is not only eye-catching but also helps the viewer retain the information presented for longer.

8. Theme

A unified theme ensures every part of your design is consistent and follows a standard. This should happen naturally if you have taken care of the aforementioned design principles. You can incorporate a theme for your company, or based on the niche of the visualization. This helps connect with the user on a deeper level and augments the visual design.

We’re now witnessing a massive explosion in the quantity of data and the applications of it. Data analytics and visualization are among the top use cases of big data and many businesses are bringing out interesting data visualizations for their internal business analyses as well as for media exposure.

Data visualizations are also among the high performing content types for the popular media sites nowadays. By following these basic design principles, analysts and reporting professionals can communicate the insights in their data more effectively, which translates into a win-win situation for the creators as well as consumers.

Disclaimer: We may link to sites in which we receive compensation from qualifying purchases. We only promote products and services that we believe in.

Continue Reading