Normalization leads to many essential and desirable characteristics, including aesthetic pleasure. Besides it is also theoretically "correct". In this context, denormalization is applied as a compromise, a correction to achieve performance. Is there any reason other than performance that a database could be denormalized?
Denormalization normally means some improvement in retrieval efficiency (otherwise, why do it at all), but at a huge cost in complexity of validating the data during modify (insert, update, sometimes even delete) operations. Most often, the extra complexity is ignored (because it is too damned hard to describe), leading to bogus data in the database, which is often not detected until later - such as when someone is trying to work out why the company went bankrupt and it turns out that the data was self-inconsistent because it was denormalized.
I think the mantra should go "normalize for correctness, denormalize only when senior management offers to give your job to someone else", at which point you should accept the opportunity to go to pastures new since the current job may not survive as long as you'd like.
Or "denormalize only when management sends you an email that exonerates you for the mess that will be created".
Of course, this assumes that you are confident of your abilities and value to the company.
Mantras almost always oversimplify their subject matter. This is a case in point.
The advantages of normalizing are more that merely theoretic or aesthetic. For every departure from a normal form for 2NF and beyond, there is an update anomaly that occurs when you don't follow the normal form and that goes away when you do follow the normal form. Departure from 1NF is a whole different can of worms, and I'm not going to deal with it here.
These update anomalies generally fall into inserting new data, updating existing data, and deleting rows. You can generally work your way around these anomalies by clever, tricky programming. The question then is was the benefit of using clever, tricky programming worth the cost. Sometimes the cost is bugs. Sometimes the cost is loss of adaptability. Sometimes the cost is actually, believe it or not, bad performance.
If you learn the various normal forms, you should consider your learning incomplete until you understand the accompanying update anomaly.
The problem with "denormalize" as a guideline is that it doesn't tell you what to do. There are myriad ways to denormalize a database. Most of them are unfortunate, and that's putting it charitably. One of the dumbest ways is to simply denormalize one step at a time, every time you want to speed up some particular query. You end up with a crazy mish mosh that cannot be understood without knowing the history of the application.
A lot of denormalizing steps that "seemed like a good idea at the time" turn out later to be very bad moves.
Here's a better alternative, when you decide not to fully normalize: adopt some design discipline that yields certain benefits, even when that design discipline departs from full normalization. As an example, there is star schema design, widely used in data warehousing and data marts. This is a far more coherent and disciplined approach than merely denormalizing by whimsy. There
Denormalized data is much more often found at places where not enough normalization was done.
My mantra is 'normalize for correctness, eliminate for performance'. RDBMs are very flexible tools, but optimized for the OLTP situation. Replacing the RDBMS by something simpler (e.g. objects in memory with a transaction log) can help a lot.