Data Modeling and the eternal struggle between business & IT

In a previous article that I published on this platform, I talked about the confusion that I observed in my first couple of weeks in the industry, between some very important and meaningful terms: model, schema and metadata. In that article, I tried to alleviate some of that confusion by offering some important context and clarifications. In this article, I will try to build on that.

Part of the confusion in the terminology stems from the fact, I believe, that in traditional relational database management systems, your physical model and your schema were tied at the hip. It would be extremely weird to have one without the other. In fact, in relational data modeling, your schema would literally be the output of your physical modeling and would therefore be a different representation of the same thing. The physical model would be human readable, and the schema would be machine readable. That all changed with NOSQL database management systems (document databases like MongoDB or graph databases like Neo4j), where you could have “the data be the model”, and where the enforcement of the model would be completely optional and usually not even considered before one would take the system into production.