The 3 concerns in data modeling, and their value

In a previous article, I have already alluded to the fact that I think that the confusion around the three words (model, schema, and metadata) is really important to clarify and get straight for our industry. That confusion has a historical reason: in the past 4 decades, when Relational DBMS’ ruled the earth, there was no real need to be specific around these three different concepts: the model and the schema were basically intertwined. But that has changed with NOSQL, and today we do really have to be more specific because we have more variability – we can have more or less modeling, and more or less schema, as we choose.  Tools like Hackolade and its polyglot data modeling have already made that very clear as well.

The main reason why I think being specific around this is important, is because it also allows us to be specific about the underlying concerns that speak to these words. 

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.

Confusing words in Data Modeling

For a few weeks now, I have been helping out my friends at Hackolade with some really interesting work around their core product, the Hackolade Studio and how to make it even more successful in the marketplace. This means talking to a LOT of people - both current customers, active users, partners, friends in the industry - and more. It’s been fantastic to talk to so many interesting people, and to learn so much from their insights.

During these conversations, I have noticed that there’s quite a bit of work to be done to clarify and straighten out the meaning of the words that we use in these conversations. I have noticed that in the NOSQL data modeling space, we are not always very precise with our words - and that this imprecision can lead to all kinds of misunderstandings. Specifically, I have been struck by the confusion around 3 words: model, schema, and metadata.