And even so, while there are a lot of countries and languages, a collection of tests in all of them, which is all it takes for these examples, is not "big data". Again you are crediting analytics for things that people were fully capable of and responsible for doing in the days without traffic data. I realize it is great for the resume and sounds sexier to do "analytics" as opposed to just basic software testing (you should see what people are calling "AI" in other industries these days). And I'm certainly happy to agree that analytics has some value, say in improving wording and stuff that a single instance won't tell you, but again these aren't cases of that.
Similarly with languages, that you cans er from backend metrics.
More data seems always nice, but analyzing more data isn't making analysis simpler and there is a big privacy impact in analytics, especially when outsourcing to data collectors, who can gather data across sites. (Which then also gives Google information which services are interesting to user and can be integrated into search etc.)
A button turns out to be below the fold on common small screen sizes that a new designer forgot to consider. A bad translation results in 90% of users in a particular country misunderstanding something. A JavaScript library doesn't work on a common Android phone you don't test with. Latency issues make something virtually unusable from the other side of the country because of a single badly written function that's easy to rewrite -- but you still have to catch it!
You need ALL forms of feedback. It's not a question of some being more important than others. They're all important and play their own unique roles. Analytics is for catching AND debugging all the things that go wrong at scale in the real world, as opposed to the artificial and limited environments used for user testing.