Way back in the 1960s databases didn’t separate data representation and data access.
To navigate in an index, a database user had to know the physical structure of the index.
Obvious deficiencies of the approach led to introduction of separation of data model and data representation. Relational model is one and still the most popular way to do it.
One of the most well known deficiencies of a relational model is the so-called object-relational impedance mismatch: there is more than one way to map objects to relations, and none of them fits all access patterns well.
It has as well a number of advantages: simplicity, ease of analytical processing, and, let’s not forget, performance: by normalizing data, a user is forced to tell the DBMS more about data constraints, distribution, future access patterns.
This makes building efficient and to-the-point data representation structures easier.
Unfortunately, the past generations of database management systems did not address one of the main architecture drawbacks, which plagues the relational model: rigidity of schema change. Very few mainstream DBMS allow to change the structure of a relational database quickly, without downtime or significant performance penalty. This is not a drawback of the relational model, but of one which relates to the implementation.
It should also be kept in mind that in many cases a relational model is an overkill, and a simple key to value mapping is sufficient.
And of course no single model can fit all needs (e.g. graph databases build around the notion of nodes & edges, yet, good luck trying to quickly calculate CUBE on a bunch of nodes in a graph database).
Unfortunately, the world of NoSQL, when it comes to the data model, often simply takes us back to the 60s: there is minimal abstraction of data access from data representation, and once a certain representation has been chosen, there is no way to change it without rewriting your application (e.g. to fit the new performance profile).
Scalability is an answer, but a silly one: throwing more hardware at a problem is not always economical.