Art meets science

Data visualization guidelines

Turn numbers into narratives

Data visualization tells multilayered stories to engage decision makers in deep exploration.

Pie chart animating to different sizes Pie chart animating to different sizes


Personal touch

Visualizing data is central to this key moment in time, when the borders between big and impersonal, and small and intimate data will blur as we’ve never seen before. The greater the quantity and kinds of data collected, the more we need to experiment with how to make it unique. Instead of starting from standards, begin from a blank page and experiment with a custom visualization. Even if you come back to the basics, small details from your process and play can enhance basic charts to reveal more about topics users are interested in.

Deep engagement

Business intelligence tools lead people to believe that the ideal process to create visualizations is to load data in a tool, pick from among a list of out-of-the-box charts, and get the job done in a couple of clicks. Yet, simplified solutions are rarely able to frame hard-to-define problems, let alone solve them. User behavior tells us that decisions aren’t based on any one vis or single piece of information. Instead, we tend to compare and synthesize many types of data before coming to a conclusion. Because of our bias towards analysis, clarity not need come all at once; it is in our search itself where we often derive meaning.

Embrace imperfection

Data-driven doesn’t mean unmistakably true because data and the tools that collect it are human-made. Data is not pure fact, but evidence that filters reality in a very subjective way. It has the unique power to abstract our world and help us understand it, according to relevant factors that are different or constantly changing. A visualization is not intended to be black and white, but good enough for exploration, because it delivers meaning to the people reading the information instead of getting hung up on the numbers.

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