Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only through the lenses of word authority rather than with our own eyes.What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather the task of the designer is to give visual access to the subtle and the difficult–that is, the revelation of the complex.
Example: A customer may send over one billion messages. Showing rates with 3 decimals is necessary to provide the needed precision. A customer with only ten thousand sends only needs rates in 2 decimals places to get a similar level of precision.
Example: Take the above Undelivered graph. What story might the data be telling? A history of 0 messages created followed by a slow increase tells us this customer is most likely integrating push. We know that when a push fails because the token is invalid or unregistered, we generate a bounce. Then, further messages are not sent and marked as suppressed. Seeing this relationship in rates combined with a decrease in bounce rate could mean a customer is fixing their issue with invalid tokens.Now what if we know that’s a common story and we can codify it? Maybe it’s: X days of 0 messages sent, followed by 0 > n < 100 messages sent and >50% failure rate. If we see it beginning to unfold for other customers, we can alert them to links for debugging their integration.
Example: Consider a count of email messages sent per month for the past year. That data will most likely be comprised of high numbers (in the hundreds of thousands) with a small magnitude of difference. So while, a bar graph will show those counts over time, it will be hard to visualize the month-over-month difference as a bar graph’s y-axis requires equal increments starting at 0. To better visualize the trend in monthly send volumes given that data, we could show:
- A line graph depicting the counts with an adjusted y-axis (y-min to y-max),
- A line graph depicting percentage change month-to-month,
- Or something else entirely.When showing this data, we have to take into account the fact that the data is made up of large numbers with a small magnitude of difference.
Example: Consider our spam rate threshold. When a spam rate goes above a certain level, we alert them to potential deliverability issues with a tooltip. That’s great, but it’s reactive.By including that rate on a graph showcasing spam rate over time, we’re able to be proactive. A customer can now see the direction their spam rate is trending and can proactively take action.
Example: Consider a bar chart covering the past 9 quarters and a customer wanting to compare Q4 over the past 3 years. Adding a button within a tooltip allowing a customer to add that datapoint to a table below the graph enables them to directly compare those specific data points.
Example: a customer makes significant changes to the campaign and wants to add an annotation so they can visualize the difference in performance before and after the change.