How can you optimize data visualization to reduce cognitive load?

1 Define your goal

Before you start creating a data visualization, you need to define your goal. What is the main message or insight you want to convey? Who is your audience and what are their needs and expectations? How will they use the data visualization and in what context? These questions will help you narrow down your scope, select the most relevant data, and choose the most appropriate format and style. For example, if your goal is to compare the performance of different database servers, you might use a bar chart or a table. If your goal is to show the geographic distribution of database users, you might use a map or a heat map.

2 Simplify your design

One of the key principles of data visualization is to simplify your design as much as possible, without compromising the meaning or accuracy of the data. This means eliminating any unnecessary or distracting elements, such as excessive colors, fonts, labels, grid lines, borders, or legends. It also means using consistent and intuitive visual cues, such as size, shape, position, or orientation, to highlight the most important or relevant data points. By simplifying your design, you can reduce the cognitive load on your audience and help them focus on the main message or insight.

3 Use preattentive attributes

Preattentive attributes are visual features that can be perceived and processed by the human brain in less than 200 milliseconds, without conscious attention. They include color, brightness, contrast, motion, orientation, and shape. You can use preattentive attributes to draw attention to specific data points, groups, or patterns, and to create visual hierarchy and structure in your data visualization. For example, you can use color to differentiate between categories, brightness to indicate magnitude, contrast to emphasize outliers, motion to show change over time, orientation to show direction, and shape to show type.

4 Apply Gestalt principles

Gestalt principles are psychological laws that describe how the human brain perceives and organizes visual information. They include proximity, similarity, continuity, closure, and symmetry. You can apply Gestalt principles to improve the readability and coherence of your data visualization. For example, you can use proximity to group related data points together, similarity to show common characteristics or relationships, continuity to show smooth transitions or connections, closure to complete incomplete shapes or forms, and symmetry to create balance and harmony.

5 Choose the right scale and format

Another important aspect of data visualization is to choose the right scale and format for your data. The scale and format can affect how your audience interprets and compares the data, and whether they can spot any patterns, trends, or anomalies. For example, you can use a linear scale to show proportional changes, a logarithmic scale to show exponential changes, or a categorical scale to show discrete values. You can also use a numerical format to show precise values, a percentage format to show relative values, or a graphical format to show qualitative values.

6 Test and refine your data visualization

The final step of optimizing data visualization is to test and refine your data visualization. You can test your data visualization by asking yourself or others some questions, such as: Does it convey the main message or insight clearly and accurately? Does it answer the questions or meet the needs of the audience? Does it use the appropriate type, style, and level of detail? Does it avoid any errors, biases, or misinterpretations? Does it follow the principles and best practices of data visualization? Based on the feedback and results, you can refine your data visualization by making any necessary adjustments or improvements.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?