![]() This can be especially useful in instances where you can’t put the most important stuff leftward or at the top (because other constraints dictate the ordering). Where are your eyes drawn? Similarly to how we focus attention thoughtfully in graphs as part of explanatory analysis (something we’ll explore in detail in Chapter 4), we can also focus our audience’s attention in tabular data to establish hierarchy of information. If revenue were more important than accounts, I could move the two revenue columns leftwards, but I can also use other ways to focus attention there. I want to keep the distribution of accounts and percent of accounts next to each other since those relate to each other. Going left to right, I’m happy enough with the way it is structured. In this particular example, I’d sort my tiers starting with the top (that is indeed A+) and decreasing as we move down the table. ![]() In other words, if there are super-categories or data that needs to be taken into account together, keep them in the order that makes sense. When we think about applying this to how we design our tables, it means you want to put the most important data at the top and at the left-when you can do so in the context of the overall data in a way that makes sense. These are some specific tips-I’ll also put forth a couple of more general ones: consider the zigzagging “z” and where your eyes are drawn.Ĭonsider the zigzagging “z”: Without other visual cues, your audience will typically start at the top left of your visual (for example, your table) and do zigzagging “z’s” across to take in the information. Doing so also allows me to make the columns narrower so the table overall takes up less space. I can group the accounts-related columns and revenue-related columns with a single title (and under that, number and percent), which will reduce some redundancy of titles and also give me more space to be specific about what the columns represent. In the case of tables, however, I do sometimes opt for center alignment because of the separation this creates between columns (another common practice in tables is to right-align numbers or align by decimal point, which allows you to easily eyeball relative size). Speaking of white space, I typically avoid center-aligned text in graphs (because it creates hanging text and jagged edges that look messy) in favor of left- or right-aligning text. I recommend against shading every other row and instead am an advocate for white space (and limited light borders) to set apart columns and rows as needed. When tables are designed well, the actual design fades to the background so that we focus on the numbers in a way that makes sense. STEP 2: There are additional improvements I can make to this table. There we are already summarizing in millions and it seems like we would lose important differences between the dollar volumes by rounding to a whole number, so there I’d round to one digit past the decimal point.įigure 2.1b is an improved table that addresses the preceding points. Here, given the scale of the numbers and differences between them, I would round to whole numbers across all except the fourth column depicting revenue. Is the difference between 7.08% and 7.09% meaningful? If not, we can drop a digit by rounding. This can make the numbers themselves harder to interpret and recall and may convey a false sense of accuracy. There isn’t necessarily a single “right” answer, but you want to avoid too many digits of significance. When showing data like this, you should be thoughtful about the appropriate level of detail. When I focus on the numbers themselves, two digits of significance (places past the decimal point) seem like a lot for the % Accounts column given the scale of the numbers. If they are, then there must be some “Other” or “Non-tier” category that I’d want to include in order to have the full picture. So now I’m unsure whether these really are percent of total or something else. The final column (% Revenue)-which I assume means percent of total revenue-sums to 95%. The third column (% Accounts)-which I assume means percent of total accounts-sums to 81.16%. In fact, it’s when I start to do that when I notice some bigger issues. I wish there was a “Total” row at the bottom, because in the absence of this I find myself wanting to add up numbers. ![]() In terms of questions, I wonder if the tiers are in order: I would think A+ belongs above A and am confused that they don’t appear that way in the table (perhaps due to alphabetical sorting?). In terms of specific observations, I might start by noticing that the majority of accounts are in Tiers B and C, while Tiers A and A+-though they don’t make up a huge number (or percentage) of accounts-do make up a meaningful amount of revenue. STEP 1: When I encounter this table, I start reading and scanning down columns and across rows. The following details how I approached the exercise. ![]()
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