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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…

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