People also tend to believe that data modeling is at the end of the road, say no more. What could possibly be done to make it more productive and produce higher quality? It was developed in the 1970s, mind you. How much else from the ’70s has survived? (Well, just teasing: Relational modeling survived …)
As for any theory, you will have to challenge the assumptions. Data modeling, as we know it now, is very engineering-oriented with complex diagrams, which are not too intimate with the best wishes of israel whatsapp number data its consumers. In many ways, it is still “blueprints” based on axiomatic paradigms such as database normalization, etc. – meant for the construction of physical constructs such as databases. The exception is on the informatics side of the house, where semantic models (graphs) have quite some success because of expressiveness, precision, and relative ease of use (read: “knowledge graphs”).
Strong,
So, is this the end of the journey? Will JSON take over the whole gamut of data models?
I think not. Data modeling, with semantics, is an open-ended research area. Traditional computer science-based data modeling was based on rather narrow axioms and paradigms – supposedly strengthened by logic and abstractions.
But semantics and cognition open the door to a very large universe of discourse. In fact, what data modeling tried to do over the years was to get into the realms of cognitive science (psychology, clinical, and philosophical).