How do you measure DevOps success? Talk “metrics” for DevOps work or cloud-native infrastructures, and the conversation tends to revolve around familiar operational and productivity measures. Uptime. Transactions per second. Bugs fixed. Commits. Familiar categories of data that are straightforward to track and would seem to at least passingly correlate with some combination of efficiency, health of the environment, and development speed.
It’s common for IT teams to instrument for and log such data – especially now that machine learning and other modern tools make it much easier to gain insights from large datasets, whether for pro-active, predictive analytics, or root cause analysis after a failure.
That’s not a DevOps metric!
However, that doesn’t make everything a metric! A metric is properly thought of as a key performance indicator for data – a measurement that’s important to you in some fundamental way.
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