In order to gain traction and acceptance among users, dashboards must visually flag metrics that are underperforming, overperforming, or behaving in other ways that warrant attention. If a dashboard doesn’t flag metrics, it becomes very time-consuming for users to review the dashboard and spot metrics that require attention among a potentially large number of metrics, and metrics that urgently require attention risk going unnoticed. In previous blog posts, I discussed several common ways to determine which metrics to flag on a dashboard, including good/satisfactory/poor ranges, % change vs. previous period, % deviation from target, and the “four-threshold” method. Most of these methods, however, require users to manually set alert levels for each metric so that the dashboard can determine when to flag it, but users rarely have the time set flags for all of the metrics on a dashboard. Techniques from the field of Statistical Process Control can be used to automatically generate reasonable default alert levels for metrics that users don’t have time to set levels for manually.

In order for a dashboard to gain traction among users, it must visually flag metrics in order to draw users’ attention to metrics that require it. Unfortunately, though, the most common methods of flagging metrics on dashboards (“Vs. previous period,” “Single-threshold flags,” “% deviation from target flags,” and “Good/Satisfactory/Poor ranges”) are prone to several problems: they often flag metrics that don’t require attention, fail to flag metrics that do, and can be slow to visually scan. In this post, I discuss the “four-threshold” method that I now use, since it doesn’t have these shortcomings.

This post is an excerpt from my upcoming book, Practical Dashboards, and is the seventh in an eight-part series of posts on how to determine which metrics to visually flag on a dashboard.

This excerpt from my upcoming book, Practical Dashboards, is the sixth in an eight-part series on how to determine which metrics to visually flag on a dashboard (i.e., with alert dots, different-colored text, etc.) in order to draw attention to metrics that require it. In this post, I look at the “Good/Satisfactory/Poor” method used on many dashboards. While not as problematic as the “vs. previous period,” “single-threshold,” or “% deviation from target” methods that I discussed in previous posts, this method still has several serious drawbacks that become obvious when pointed out. In the next post in this series, I’ll introduce a more useful approach called “four-threshold” visual flags.

This excerpt from my upcoming book, Practical Dashboards, is the fifth in an eight-part series on determining which metrics to visually flag on a dashboard (i.e., with alert dots, different-colored text, etc.) in order to draw attention to metrics that require it. In this post, I look at what I call the “% deviation from target” method of flagging metrics on a dashboard. I explain why, despite seeming like an improvement upon single-threshold flags, and being used on many dashboards, “% deviation from target” flags can easily mislead. In a later post in this series, I’ll introduce a more useful way to flag metrics on dashboards called the “four-threshold” method.

This excerpt from my upcoming book, Practical Dashboards, is the fourth in an eight-part series on determining which metrics to visually flag on a dashboard (i.e., with alert dots, different-colored text, etc.) in order to draw attention to metrics that require it. In this post, I look at the “single-threshold” method of determining which metrics to flag and why, despite being extremely common, this method has several major drawbacks that become obvious when pointed out. In a later post in this series, I introduce a more useful approach called “four-threshold” visual flags.

This excerpt from my upcoming book, Practical Dashboards, is the third in an eight-part series on how to determine which metrics to visually flag on a dashboard (i.e., with alert dots, different-colored text, etc.) in order to draw attention to metrics that require it. In this post, I look at the “vs. previous period” method of flagging dashboard metrics and why, despite being extremely common, this method for drawing attention to metrics can be worse than useless. In a later post in this series, I’ll introduce a more useful approach called “four-threshold” visual flags.

This excerpt from my upcoming book, Practical Dashboards, is the second in an eight-part series of posts on how to determine which metrics to visually flag on a dashboard (i.e., with alert dots or different-colored text) in order to draw attention to metrics that require it. Determining when to flag or not flag metrics on a dashboard can be a messy process within organizations because people often disagree about what should be considered “good” or “bad” ranges for a given metric. There’s another, less obvious cause of controversy in such discussions, though, which is that people often talk about two very different types of flagging criteria without realizing it: criteria for indicating when action is required and criteria for indicating whether a metric is performing well or not. While these might sound similar, their fundamental purposes and the ways that we go about setting them are very different.

This excerpt from my upcoming book, Practical Dashboards, is the first in a eight-part series of posts on how to determine which metrics to visually flag on a dashboard (i.e., with alert dots, different-colored text, etc.) in order to draw attention to metrics that require it. In this post, I briefly discuss why visual flags are almost essential in order for dashboards to deliver the user traction, satisfaction and value that organizations hope for and expect, and why a lack of visual flags has contributed to the failure of many dashboards.

I’ve seen a lot of dashboards that failed to meet users’ and organizations’ expectations. There are a variety of reasons why this happens and, in this video and post, I focus on one of the most common ones, which is that the people who designed the dashboard didn’t fully understand the distinction between problem scanning and performance monitoring. When this happens, the dashboards that they end up designing don’t fulfill either of these needs well. This video and post are based on a chapter from my upcoming book, Practical Dashboards.