From my view, it is generally not a good option for cases it wasn't designed for, an example being non-analytical reporting. If you are running operational support, getting the source data immediately and aggregating/displaying can be more helpful than modeling for analytics workloads. The line between these is blurred in most orgs. To the OPs point, data modeling seems like a sidenote in most analytical discussions. You can accomplish a lot using the star model which is essentially:

Prepare things to be fast by sorting them into proper groups (fact/dim/bridge)

Rely on ints

Store atomic data

Provide summaries/aggregates

Model after the questions you ask; not the system it comes from

But for a lot of things it works pretty well at what it does. It's useful to have a body of work formed over many years, tested in many demanding scenarios, providing some sort of structured guidance. Twitter and blog posts and random tinkering don't rise to the same level.