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It does indicate a culture where there aren't massive barriers to getting changes in. I worked at one startup on the AI team, and we were allowed to deploy at
by not_a_moth 6y ago
It does indicate a culture where there aren't massive barriers to getting changes in.
I worked at one startup on the AI team, and we were allowed to deploy at will, we iterated constantly, pushed multi times a day, and had a flat hierarchy. We weren't under the political umbrella of the engineering and product groups. It was definitely risky but no major issues ever came from it and we gained the trust of DevOps team and our CTO.
The back end team, different managers, was very hierarchical, had to schedule deploys well in advance and inside certain time windows, and non technical engineering managers would monitor and send summary emails to everyone after each deploy.
Guess what, that back end team sucked. Their timelines for features were embarrassingly long, they watched the clock, their managers loved to booze, they overcomplicated the architecture and never produced anything innovative for the company.
Deploys can signal culture.
- rcfox 6y agoDeploys is perhaps a good metric for engineers looking at managers, but not managers looking at engineers. (And definitely not managers looking at managers!)
- Ididntdothis 6y ago“Deploys can signal culture.“ They can signal culture maybe. But if you tell your back end team that you think their culture is bad because of not enough deploys they will crank up the number of deploys without fixing any of the deeper issues. All metrics like number of deploys , tickets closed , story points, number of commits are interesting data points but you should never try to optimize for them. Doing so signals lazy management that likes to beat around the bushes.
- majormajor 6y agoNumbers tell you what is happening, not why it is happening. If you see interesting numbers, you need to figure out the why, before just trying to move the numbers.
- darkerside 6y agoJust a bit of devil's advocate here, but is it possible that the problems they were solving were much more complicated than those on the AI team? Obviously I have little insight into the actual work, but there are some hints. Long timelines indicate difficulty interesting features into an already complex codebase. Leaving the office early can indicate that low quality hours had a higher downside risk of introducing problems into a complex situation. Pure speculation here, but I know that architecture can look overcomplicated when it's actually just solving a complicated problem.
- deleted 6y ago[deleted]
- onion2k 6y agoDeploys can signal culture. I worked at a company that deployed multiple times a day too. There were no automated tests and the QA process was terrible, so most of the deploys were bug fixes. Deploys can signal culture, but not always in a good way.
- ashtonkem 6y agoIt can also signal a lack of process and uncertainty about required features. A team that deploys a ton due to mistakes and unused features might end up less effective than a team that deploys more commonly used features with a more effective testing and verification harness. This is why it’s hard to boil it down to a few simple metrics.