In my previous post on data modeling, I wrote that it is an art and not pure science. In this post, I’ll elaborate on that.
Today I gave a small workshop on dimensional modeling for the business intelligence team that I am currently part of. The main objectives of this workshop were to get the team in a standard way of working and to refresh their memory on dimensional modeling.
As preparation I had written a small summary about dimensional modeling, mostly based on Dr. Ralph Kimball’s book The Data Warehouse Toolkit.
Before I got into presenting some example models for the project I’m working in, I had a small Q&A session about the preparation material that I had provided.
Well, I thought it would be small…
I received many design questions that I could not answer… in the way they thought I would answer it, i.e. as a straight answer that would set it for once and for all.
That is exactly what designing a data model is all about, especially when doing dimensional modeling. To quote Ronald Damhof:
it all depends on the context
That is also exactly why there are some drawbacks in dimensional modeling compared to other modeling techniques such as data vault.
Is a particular attribute dimensional or a measure that belongs in the fact table? It just depends in how it will be used. There is no scientific answer to it.
That’s why I say, data modeling is an art. Art represents the artist’s interpretation or view.
It is also the reason why I prefer data vault over dimensional modeling for the EDW. There is less left up to the interpretation. But that’s another story…