Welcome to Part 3 of our series on data visualization! Follow along as we journey through the world of turning data into easy to understand images. (Journey back to Part 1 of the series)
Information overload happens when data gets in its own way. If information is not easily grasped with the implications understood, it will be ignored or potentially misinterpreted. The result is slow, poor, or uninformed decision making.
Ensuring accessibility and ease of interpretation is the second concept in RG+A’s approach to data visualization. We make our graphics more easily digestible because we:
- Provide context (Compare to meaningful benchmarks)
- Remove distractions (Remove chart-junk)
- Direct attention (Use color to call attention to key factors)
If we collect survey data suggesting that the average person eats an apple daily, we could create a very clear, accurate graphic that communicates this specific information. And while informative, this fact doesn’t aid understanding or interpretation – “An apple daily. So what?” The data does not exist in a vacuum. We want to show comparisons to a meaningful benchmark to help put our data into context.
Perhaps our hypothetical survey also collected data on frequency of orange and banana consumption. And maybe we also have real-world data on peach and pear consumption. If we can make these data align with similar populations and chart the overall result, it would help communicate not only how frequently the average person eats an apple, but what that means relative to the rest of the market.
According to Edward Tufte, a noted pioneer in the field data visualization, “A large share of ink on a graphic should present data-information, ink changing as the data change. Data-ink is the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented.” More simply, removing extraneous information focuses attention on the points that matter.
Color use in data visualization is the subject of entire, but we will briefly discuss use of color here. Just as data does not exist in a vacuum and we consider its context, it is the context surrounding color selection that matters when directing attention. Is the color different from the surrounding shades (warm vs. cool colors, or colors on the opposite side of the color wheel)? Is the color a different intensity than the surrounding shades (do they appear different if you view your document in greyscale)? When the answer to these questions is “Yes,” then high differentiation using color exists.
We use this differentiation to direct attention to specific portions of the visualization upon which we wish to focus. This can be executed with a couple of tactics:
- Increase contrast (make sure at least one of the questions above is “Yes”) between the key comparison and the rest of the data
- Use a pop of color (highly differentiated – “Yes” to both questions above) to call out specific points of interest
We’ve moved from the granular (truth of data display) to something more complex (comparisons to show data in context). The next blog post will discuss how RG+A combines multiple visualizations and components to tell a story.
What tips do you have for making data visualization impactful? What challenges have you encountered?
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See you next week for Part 4 of RG+A’s data visualization series: Telling a Story