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.