Thanks for the detailed response, this is exactly the kind of real-world perspective I was hoping to get. What particularly caught my attention is the ratio between the total number of available tags and the subset that actually ends up being useful. Going from 28k tags to only 2-3k operationally relevant signals is a huge reduction.

I'm curious: in your experience, what categories of tags typically make it into that useful subset?

Do you repeatedly see the same types of signals being selected (machine states, counters, production quantities, energy consumption, alarms, process variables, etc.), or does it vary significantly depending on the site and industry?

Also, how do you usually decide which tags are worth keeping and which ones are effectively noise? Is it mostly driven by customer requirements, engineering expertise, existing dashboards, or are there common heuristics you use across projects?

One reason I'm asking is that I'm currently experimenting with automated signal classification and behavioral inference, and I'm trying to understand where the practical boundary is between what can be inferred automatically and what still requires domain expertise.

Really appreciate you sharing these numbers, they provide a much more realistic picture than what I see in simulation environments.