Very true.
However -- note that the process of implementing OKRs has caused the leadership team to write down their expectations, which previously might have been unspoken. This is a first step towards resolving the problem. The next step is for the CTO to push back, hard, on the bits of these that aren't actually realistic, and hopefully get the leadership team aligned on what can actually be done. And so, the process of OKRs potentially has value here in flushing out unrealistic or mismatched expectations between different parts of the org.
This is one of those rare "my way or the high way" moments in leadership; as CTO at this company it's your job to either get the leadership team to realize that they are asking you for more than you can be expected to deliver, or quit. You can't stick around and put your name on a plan that you know is impossible; otherwise you're going to be the one that failed for every quarter to come. And even worse, you can't sign your team up for this BS. It's your job to shield them from this kind of shit.
> Half the Objectives were outside the scope of the engineering team.
Shared OKRs are difficult, but sometimes they are unavoidable. The most difficult business problems usually are cross-functional. At their best, OKRs can help to make these cross-functional dependencies more explicit, and foster communication and collaboration around them.
If they are trying to have engineering take full ownership of a shared OKR, that's a big problem. But if you clearly call out the shared ownership, and consider both parties responsible for implementation, then I think that's OK.
> We were to going to increase our release cadence 10x. We were also going to reduce production issues by 100%. That's right, our objective was 0 production issues whilst massively increasing our release cadence with 0 extra resources.
As a tangential point, increasing release cadence can definitely decrease your long-term rate of issues (see "Continuous Delivery" by Humble[1]) -- this forces you to automate manual processes, and manual processes are one of the main places that errors creep in. Though I think "count of issues" is a very poor metric, you're better off with an uptime metric. And "100%" is the only strictly-incorrect number to pick for uptime, because it is literally impossible; my (super-unscientific, don't hold me to this) rule of thumb for business people is "every extra 9 costs you 10x". So do you need 99.9% uptime or 99.99%?
[1]: https://www.amazon.com/Continuous-Delivery-Deployment-Automa...
You start covering the most essential needs just so they become useful and usable. Then you go ahead and iterate from there, building something which suits your team, company & product. Learning and adapting in the process.
[1] https://www.amazon.de/Continuous-Delivery-Deployment-Automat...
i've been reading through this book and it does a decent job of covering the principles for CICD, builds, tests releases: https://www.amazon.com/Continuous-Delivery-Deployment-Automa...
you'll run across various "thought leaders" in devops and its important to remember that a) each employer treats devops and cicd differently and you'll want to learn their practices as you bring about your own ideas to the culture and b) form your own opinions, just b/c thought leaders and books are out there its important to learn what you like to do and improve how you like to do it.
Continuous Integration is fundamentally about creating a tight feedback loop between your developers and your code. When you program in your IDE, the instant you write uncompilable code, you get red squigglies, so you're getting an instant feedback loop on something you just wrote.
CI is the same thing, but at a higher level. The instant you commit your code, some automated process should take over and start analyzing / compiling / testing your code and look for things to give you feedback on. If your code doesn't even compile -- one of the first milestones of CI, you should know that immediately.
Since you just committed it, making the fix is easy. This is compared to a developer who downloads your code the next day, can't compile, comes and bugs you about it, etc...
As far as some real world use cases, we just setup Jenkins for a new Java project we're writing. It does an automated build test that compiles and executes all unit tests automatically on any commit to GitHub on any branch. It's a little slower than I like -- our still growing app takes a full 3 minutes to compile and give feedback.
But, it's been great. For example, the GitHub client on Mac OS X doesn't recognize when I change uppercase letters to lowercase and vice versa, so while my local compiles worked fine, my repo actually had a failing build. Once I committed, I got an automated email within 5 minutes telling me the build failed, and I fixed it. Without CI, I may not have found about that issue for weeks, making the change more difficult.
For production deployment, we're still in alpha, but we've got a 1-button push to deploy. Again, slower than it should be -- in this case 5 minutes -- but the automation is awesome and makes doing any deployment -- whether hot fixes or new releases that much more pleasant.
Regarding the performance, I see it as a win just to get anything automated, however slow it may be. Because once you're there, you can always look for ways to optimize it. For example, our current build process, re-downloads dependencies every single time. This could clearly be cached. When it's a priority for us, we'll do it.
Specifically to understand the benefits of CI/CD I recommend reading Jez Humble's book on the topic[0]. He's an excellent writer on the topic. You can also google his name and watch some excellent conference talks from him.
The best way to understand CI/CD is through experiencing an organisation that is building and deploying at scale. But you're between a rock and a hard place if you can't find employment without that experience. Jez Humble's books and talks will help you understand the benefits and capabilities of these systems.
[0] https://www.amazon.com.au/dp/0321601912