![]() ![]() The following two views (colored the same as the final two iterations above, respectively) reflect these changes. To direct attention to higher error rates first, I’d start with those and order by decreasing error rates as we move from left to right across the heatmap. Due to both of these reasons, I’ll round to the nearest hundredth (if we go further than that, we’ll lose the ability to differentiate between many of the numbers given their magnitude).Īlso, if there’s not a compelling reason to keep the sites ordered in the manner they are currently, I’d be inclined to order them according to the error rates. If these are manufacturing sites, we’re not likely evaluating every single unit made for errors, but rather some sample, which means there’s natural error to our numbers. Not only does this many decimal places make the numbers difficult to talk about (“Site C has the highest monthly error rate over the past six months, averaging oh point three six four percent per month”) but it’s also not likely that this is true precision. In terms of other changes I’d like to make: I would advocate reducing the precision. Which of the above variations do you prefer? Are there other changes you would make to this heatmap? This final view puts clear attention on the sites with higher error rates-something likely worth noting. I felt good about this one but also tried one more version, a 2-color scale with orange for the highest value and white for the lowest (bottom right). For my third combination, I lightened the blue and reverted to white for the 50th percentile (bottom left). To adjust, next, I tried lightening the blue and moving from white to grey for the 50th percentile (top right)-this made everything look muddled. Because of their concentration (both in intensity and relative proximity), the blues stood out more than I wanted, and the white cells also competed for attention. I started with a 3-color scale, setting bright blue for the lowest value, white for the 50th percentile, and orange for the highest value (top left). I played with a few color combinations, using conditional formatting in Excel. Red-green color scales can be problematic for colorblind audience members (the most common type of coloblindness is red-green colorblind, where both red and green end up looking brownish, which can be particularly difficult if they are similar in intensity). As I walk you through my process, I encourage you to compare the different views and decide which you like best. After that, I’ll explore a couple ways to graph the data. First, I’ll take some steps to improve it. Let’s consider the heatmap at the onset of this post. That said, we can often make things easier by fully visualizing data in a graph. If you know your stakeholders will want to look up specific numbers (particularly in the case where different stakeholders will care about different numbers) and then understand them in the context of the broader landscape, a heatmap may also work in this scenario. If you are communicating to an audience who likes to see data in tables-applying heatmap formatting can provide a visual sense of the numbers without fully changing the approach (or having it feel like you’ve taken detail away). ![]() But once you’ve identified the noteworthy aspects of your data, should you use heatmaps to communicate them? They can help you start to explore and understand where there might be something interesting to highlight or dig into. Rid your world of ineffective graphs, one exploding 3D pie chart at a time.I often describe heatmaps as a good means for getting an initial view of your data. Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience.
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