A data visualization, sometimes referred to as a “data viz,” allows analysts to properly interpret data. A good way to think of data visualization is that it can be the difference between utter confusion and really grasping an issue. Creating effective data visualizations is a complex task; there is a lot of advice out there, and it can be difficult to grasp it all. In this reading, you are going to learn some tips and tricks for creating effective data visualizations. First, you'll review two frameworks that are useful for thinking about how you can organize the information in your visualization. Second, you'll explore pre-attentive attributes and how they can be used to affect the way people think about your visualizations. From there, you'll do a quick review of the design principles that you should keep in mind when creating your visualization. You will end the reading by reviewing some practices that you can use to avoid creating misleading or inaccurate visualizations.

Frameworks for organizing your thoughts about visualization

Frameworks can help you organize your thoughts about data visualization and give you a useful checklist to reference. Here are two frameworks that may be useful for you as you create your own data viz:

1) The McCandless Method

You learned about the David McCandless method in the first lesson on effective data visualizations, but as a refresher, the McCandless Method lists four elements of good data visualization:

1. Information: the data you are working with
2. Story: a clear and compelling narrative or concept
3. Goal: a specific objective or function for the visual
4. Visual form: effective use of metaphor or visual expression

Note: One useful way of approaching this framework is to notice the parts of the graphic where there is incomplete overlap between all four elements. For example, a visual form without a goal, story, or data could be a sketch or even art. Data plus visual form without a goal or function is eye candy. Data with a goal but no story or visual form is boring. All four elements need to be at work to create an effective visual.

2) Kaiser Fung’s Junk Charts Trifecta Checkup

This approach is a useful set of questions that can help consumers of data visualization critique what they are consuming and determine how effective it is. The Checkup has three questions:

1. What is the practical question?
2. What does the data say?
3. What does the visual say?

Note: This checklist helps you think about your data viz from the perspective of your audience and decide if your visual is communicating your data effectively to them or not. In addition to these frameworks, there are some other building blocks that can help you construct your data visualizations.

Pre-attentive attributes: marks and channels

Creating effective visuals means leveraging what we know about how the brain works, and then using specific visual elements to communicate the information effectively. Pre-attentive attributes are the elements of a data visualization that people recognize automatically without conscious effort. The essential, basic building blocks that make visuals immediately understandable are called marks and channels.

Marks

Marks are basic visual objects like points, lines, and shapes. Every mark can be broken down into four qualities: