Jira is great at storing, associating, and aggregating user-generated data across many dimensions for enablement of, and deep insights into, an organization's project planning and tracking workflow. However, it is not terribly good at providing important and actionable system-generated data about itself. As Jira adoption grows and scales throughout an enterprise, it becomes a business-critical production application that must be performant 24/7. We will discuss many common performance problems that he has experienced over the years as a Jira administrator, which inspired him to explore methods to track and correlate historical performance metrics and to provide actionable and predictive metrics to avoid future problems. We will also present a real-world show-and-tell for how this concept can be implemented with a machine data aggregator like Splunk.