Designing Data Visualizations

By Hailey Farris | Apr 14, 2017

This post is part of a series on visualizing data.

More and more, data is becoming a part of our everyday lives. Buzzwords, like “big data” and “data visualization”, are gaining traction, and they will likely only grow in importance as we begin to gather more data on a daily basis.

The data that users are producing can be extremely useful and valuable if made to be more digestible. Commonly found with this kind of growth, static numbers or a simple rundown of statistics can fail to embody the information you are trying to portray. With data visualization on the rise, learning how to approach it is becoming more important for creating relevant material for users and the success of your business. Design has an interesting task of taking data and making it more human.

Appropriating data into your project and using it to successfully lead a user to a specific conclusion can seem like a daunting task. Fundamentally, data visualization is a form of storytelling. An important rule to remember is that you’re creating, at some level, a biased point of view. Humans create data sets and then interpret them and assign meaning, even if a machine runs the numbers. This means that good data normally describes its biases and always provides context to tell a meaningful story.

Deciding which graph types and presentation to use depends on the data behind your visualization and the narrative it supports. This goes beyond algorithms, automation, A/B testing and analytics.

Steps for a Successful Data Visualization

Brainstorming

This is the chance to take a look at the data you have collected and begin to craft an idea as to how you would like to define your message. This is also the time to begin understanding your intended users.

Determining Your Message

Before coming to conclusions as to which data to build from, take a moment to decide what you want to say with your data. Think about what your users will do with results. Ask yourself, “What do I know, what does it mean, and why do I believe it’s important?” The chance of creating a meaningful, well-organized argument is much greater if you begin with a clear concise message.

Centering your design around context and key points of interest in the data, will get you a long way in choosing an accurate presentation that your users will be excited to consume.

Determining Your Intended Audience

How can you present the data in which to gradually lead your users to the key message? What info is most valuable to your user, and “what action(s) do I want to incite?” The better you know your users, the better the chances of creating a successful visual presentation.

Deciding Which and How Much Data to Illustrate

Illustrating data should make it more digestible. This is a chance to illustrate roles and relationships that might otherwise be obscured by too many numbers. Try not to give more information than an audience expects/needs – more is not always better. If your illustration fails to add something new to the mix or expand on a topic, it can likely be omitted. A compelling focused message is much more successful than an example that gives too much information.

Create an Effective Illustration

Once you’ve built a draft, creating an effective visualization is making sure all of its elements are labeled correctly. The visualization should still make sense even if it’s pulled from its surroundings. Great data visualization relies on the ability to effectively communicate data. There is a high priority in the ability to quickly understand what the data is displaying, how it is displaying it and how well the visualization brings certain sets of data points forward and focuses the viewer on them.

Benefits to a Successful Data Visualization

Due to the vast amount of data created and collected recently, knowing the basics of how to present that is vastly important. Following this methodology could help by displaying important analytics for the optimization, increasing efficiency and creating targeted outcomes for your business. Making data more “human” and digestible will not only increase productivity for businesses and reduce confusion, but also make day-to-day life more pleasant for users and admins alike.
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