The activity-vs-outcome distinction feels like the most interesting part of this discussion. Curious what metrics people have found most useful for separating the two.
As many readers pointed out, team velocity and process bottlenecks are a much more valuable focal point than individual developer metrics. But for this, having the ability to observe what is happening and dig deeper into the data is critical, so that you can back iterations on your improvement efforts with data.
The research is also slowly laying the foundation of what are useful metrics to track and what excellence looks like for the industry. Unfortunately, those metrics are typically difficult to measure because the underlying data often spans multiple engineering systems: Lead Time, the poster child of DORA metrics, requires data from at least your source control and your CI/CD systems.
Btw, you might be interested in checking out Faros Community Edition: https://github.com/faros-ai/faros-community-edition – an open-source engineering operations platform we’ve been building for this very purpose. Our goal is to bring visibility into engineering operations, and make it very easy to query and leverage data both within and across your systems. It’s container-based and built on top of Airbyte, Hasura, Metabase, dbt, and n8n.