Key takeaways:

As mentioned last time, at the start, it's more important to start measuring something than it is to measure the right things. Do NOT let analysis paralysis hold you back. Start measuring early to figure out what actually matters and that will also change over time.
Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.
Use fitness functions.
Data mesh really is a journey and so will be how you measure success. You will need to find small and simple ways to measure. Don't get bogged down. Your measurements will be rough and kinda depressing with the amount of challenges to tackle at the start. Just understand this is about how well you are doing, not how complete you are - there is always more to do!
Good metrics to consider for the platform success metrics: satisfaction, time to deploy new products, time to update existing ones, ease of use - which kind of blends into all the others -, searchability/discoverability, ease of interconnection, mean-time-to-detect and mean-time-to-recovery from issues, governance, guardrails, and automation/helpful artifacts.
Look to measure friction and how you reduce it and if possible - I have no great ideas here - what is the extra value add to the business, something that the platform was the genesis of? Maybe that's suggesting data products?
Lastly, reflect back on how far you've come, we often forget to do that!

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