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Cheatography

10 Golden Rules of Data Visualization Cheat Sheet (DRAFT) by [deleted]

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Be media sensitive

The reality is that many people who consume visual­iza­tions do so via more than one type of device, and often a mobile device  is in the mix. Thus, when designing data visual­iza­tions, you should beware of the form factor limita­tions and the respon­siv­eness of your visual­ization platform. Consid­ering how the visual­ization will be viewed will help you make sure your visual­ization reaches its audience.

Introd­uction

The definition of “data visual­iza­tion” often varies depending on whom you ask. For some, it’s a process of visually transf­orming data for explor­ation or analysis. For others, it’s a tool to share analytical insights or invite discovery.

Following these 10 Golden Rules will help you create the most successful data visual­iza­tions.

Begin with a goal

Starting with a goal provides the foundation to bring together ingred­ients of data visual­ization with a purpose. Whether the goal is prompting a decision or action, or inviting an audience to explore the data to find new insights, the designer is tasked with identi­fying and conveying the relati­onships and patterns of the data that best support a well-d­efined goal.

Know your data

While almost anything can be turned into data and encoded visually, knowing the context behind data is as important as unders­tanding the data itself. This knowledge will also serve to verify that you have the best data to support your goal.

Put your audience first

Data visual­ization is rarely one size fits all, and its message can be lost if it’s not customized for its audience. Thus, focus on visual­izing what your audience needs to know.

Choose the right chart

Know the strengths of each chart type and what key features of data they best visualize. Visual­iza­tions can work together if more than one is presented in story succession or on a dashboard but remember: Too many often equals too much.
 

Chart smart

The ability for a visual­ization to lead its audience to answers can also occasi­onally lead to the wrong answers. Data visual­iza­tions should not distort, mislead, or misrep­resent. Avoid cherry picking data and do not force the data to fit a message.

Use labels wisely

Give your audience context by including a simple and compelling title. Then, label axes so that they are easy to read and approp­riate to the data they display. Minimize the use of legends and other explan­atory elements, and allow the visual­ization to commun­icate without requiring additional layers of clarif­ica­tion. If you choose to use elements such as annota­tions or story points, be sure they add value.

Design to the point

Over-d­esi­gning makes important inform­ation harder to find, harder to remember, and easier to dismiss. The key to designing data visual­iza­tions is to be straig­htf­orward. Eliminate all the superf­luous chart features, unnece­ssary headers or labels, artsy details, etc. Ultima­tely, make sure everything on the visual­ization serves a purpose.

Let the data speak

The most important component of data visual­ization is the data. Use visual cues strate­gically to guide the audience and draw their attention, but let the data tell the story, not the design. A well-p­lanned narrative helps explain the data and adds depth, and aligning the visual­iza­tion’s story with the organi­zat­ion’s helps the data speak within a larger, more meaningful context.

Feedback is a good thing

Take time to fine-tune visual­iza­tions by engaging with stakeh­olders to gather feedback. Reactions from those most familiar with the data, its context, and its audience can provide a quality check before presenting or widely social­izing a new data visual­iza­tion. They might see something you don’t or have an insightful idea you could  leverage to improve the visual­iza­tion. Regard­less  of  your definition of data visual­iza­tion, the goal remains ubiqui­tous. It’s how we help people see and understand data by placing it within a visual context.