This is a measurement that tracks the time from when a user story is initiated to when it’s ready for delivery. This time can also include discussions about the user story, how long it was waiting in the backlog, and how much time it took the story to move from pickup to release.
If your lead time for changes is too high, it’s a clear sign of a roadblock somewhere in your processes, causing items in the backlog not to move along. Automating whatever could be automated will help keep your lead time low, as it shows your teams are quick to adapt to feedback and deliver on their goals.
Knowing where you spend your money will help you catch overrunning cloud expenses and waste. As we’ve already mentioned, resource efficiency is one of the pillars of platform engineering, and that’s why we encourage companies to take a sober approach to their cloud consumption.
Transparent cost KPIs will help you achieve this goal. Giving end-users and platform teams alike the ability to see the cost impact of their architecture designs before deploying or a holistic overview of all cloud expenses will be the make-or-break element in your success.
Platform Engineering KPI dashboards
Many of these KPIs have a shared responsibility between teams - keep this in mind when visualizing your metrics. Transparency and observability are one of the things that contribute to breaking down team silos, so shared comprehensive KPI dashboards are something to consider. Consider, for example, what KPIs look like at Cycloid:
Given that a third of platform teams are still struggling with the wider team’s resistance to platform adoption, keeping an eye on your adoption progress should be among your wider KPIs. After all, a platform is meant to benefit the whole company and bring teams to work closer together, and that requires cooperation on all sides.
No matter which metrics you choose, it’s important to set realistic expectations within the company during the early years of platform team adoption. Your KPIs will evolve as you move through your Platform Engineering journey, but in the early stages, you should focus on making it much easier for platform and product teams to ingest their data and make it actionable.