Here’s a case where in an attempt to maintain one graphing convention, you could accidentally introduce another. I’ve written about the challenges of deviating from common graphing conventions before. (An object’s position in space is a preattentive attribute to which we naturally assign a quantitative assessment-that is, we naturally think that “higher” means “more.”) It’s harder to conclude that the direct sales team typically closes faster than the indirect sales team because the direct line (green) is above the indirect line (blue). Usually, the vertical axis is in descending order, not ascending from top to bottom. However, at a closer glance, this chart could be confusing. I understand the logic and admit that seeing ‘good performance’ above a goal line feels right. If I had to use the word goal to label the dashed line, I’d expand it to something like “Goal: below 90 days”. I might use “Maximum” or “Upper Limit” for this specific example. To avoid this association, replace or augment these words with language that describes how to interpret the reference line instead of leaving this up to the reader. There is a natural tendency to interpret the words “goal” and “target” as a reference to a minimum acceptable amount rather than a maximum threshold. I’ll explore five ways to make it visually obvious how the goal should be interpreted. This graph challenges my standard construct of targets and goals, which could lead to confusion or, worse, the wrong conclusions if I'm not careful. In this visual, below the goal line is actually a good thing! But in the sales industry, the goal is to close a deal as quickly as possible. Targets and goals are often seen as minimum thresholds, not maximum limits. Now, pausing to think more critically about the context of this scenario, I realize I’ve misread the graph-specifically the goal line. Mostly, they fell below the goal of 90 days, exceeding their target only three times. When quickly scanning, I wonder why the direct and indirect sales teams underperformed in 2022. Show us all the magic of a great makeover! Specifically, you might identify clutter to eliminate, reflect on an appropriate chart type, determine a specific takeaway to highlight, and perhaps even use this data as the basis of a more robust story. The remit this month is a simple one: consider the following data visualization and how you can make it better. Outside of this exercise, however, please bear this facet of data visualization critique in mind and frame your approach accordingly (for more on this and related musings, have a listen to our very first podcast episode, the art of feedback). For this exercise, I release you from these considerations-in fact, I welcome you to make assumptions liberally for the purpose of your makeover (please outline those you do make in your commentary). When we critique and makeover graphs in the wild, we typically lack insight into the context and constraints faced by the original designer. Have you ever found yourself reacting with any of the preceding or similar sentiments when faced with a graph or slide? Perhaps you’ve even thought to yourself, “I could do this better!” This month, I present you with exactly this opportunity.īefore sharing the example, there is one important point I would be remiss not to mention. “That’s not the best graph for this data.”
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