The 5 IT metrics we've talked about so far are based on very simple concepts: speed and quality . The message is: deliver value to customers quickly, continuously, and frequently . The faster you go, the more likely you are to make mistakes. You should also keep an eye on the frequency of errors and the recovery time needed after a malfunction. By translating this concept into numbers, we get the 5 measurable quantities as metrics: Lead Time For Changes (LTFC): average time between the start of a process and its availability to the customer (“in production”); Deployment Image Masking Service frequency : how often news or updates are released to production; Change Failure Rate (CFR): number of production updates to recover from a problem caused by a recent update; Mean Time To Recover (MTTR): average recovery time after a serious production issue affects customers; and Availability :
Percentage of time that platform services were fully available (obtained by subtracting the sum of all recovery times). MailUp's IT metrics At MailUp , we've supported these metrics since September 2020. We've done this through automatic measurement tools and by implementing an ongoing process where: we monitor progress; we set goals ; and we identify actions to get there. This requires attention because a sudden increase in speed may be related to lower quality. It is common to forget that metrics are indirect indicators for improving a process. The Image Masking Service key is that measurement is not an end in itself. What matters is rather our impact on the process and on the product when we succeed in changing this measurement. Regarding the numbers, let's see in more detail how these metrics are calculated in MailUp: how to measure it metrics We calculate Lead Time For Changes as the average duration of the last three months that a story (or task), corresponding to a Jira issue , takes to go from start of treatment to publication. Here,
Atlassian Jira suite helps us. It allows us to measure the “time in status” of a story, i.e. how long an issue has been in a certain status for each transition in its workflow. The imported and aggregated data can then be visualized via a Jira Control Chart or ad-hoc dashboards, like the one we created with Tibco Spotfire. Problems are the building blocks Image Masking Service of any project. These can represent a problem to solve (eg a bug) or a general task for the team or some of its members. In fact, Jira software was created to not only monitor issues, but to track the entire workflow. At MailUp, we use four standard issue types in Jira: story (something the user is interested in), bug (a problem to solve), task (something to complete) and epic (a big story that can be composed in a smaller number of stories).