What Makes Good Data Visualization? - dummies

What Makes Good Data Visualization?

By Mico Yuk, Stephanie Diamond

Good data visualizations come in all shapes and sizes, but all of them have certain traits that help ensure you produce something with important insights. The following table shows the key items:

Trait Description
Useful People use it on a regular basis and can make relevant
decisions by viewing all the information they need in one
Desirable It’s not only easy to use, but also pleasurable to use.
Usable People who use it can accomplish their goals quickly and

Although these traits sound more like descriptions of a new car than descriptions of business data, focusing on these three traits for all your data visualizations should ensure that you produce something that’s not only great to look at, but that also provides value and deep insight to those who use it.

The information in the table may seem to be simple, but it’s advised that you use it as a tool to measure every data viz against, to ensure that you’re focusing on the most important items. Your main goal should be to develop a data visualization that provides key insights to its users.

Before you begin building your data visualization, you should also have some idea of what criteria make a data visualization excellent. An excellent data visualization has the following qualities:

  • It’s visually appealing. The advent of more sophisticated visual creation tools and the high quality of mobile apps have raised the bar very high on the user experience. It’s only going to get higher with the evolution of technology such as Google Glass. Your visualization will go unused if it looks like it was designed with old technology.

  • It’s scalable. If your data viz is successful, others will want to use and leverage it. Be sure to build your visualization on a system that’s scalable for accessibility and for future maintenance and modifications.

  • It gives the user the right information. It’s a problem when users focus on the visual or a particular feature and not on what they really need. Before creating a visualization, define exactly how it will be used, such as for self-service, drill-down, deep analysis, or executive overview.

  • It’s accessible. An accessible visualization is easy to use and can be modified easily when necessary. Also, the data must be accessible on any device, at any time, at any place. This feature is critical to user adoption.

  • It allows rapid development and deployment. Gone are the days of waterfall (chart-type) projects and for drawn-out data-viz deployments and builds. Users need their information today, and if you can’t provide it in a timely fashion, they’ll find other ways to get it.