I have been involved in a great on-going discussion on LinkedIn about data models. I posed the question: Is there any proven way to demonstrate data models are correct, complete, and stable with respect to the operational business and its needs?
You might enjoy joining in: http://goo.gl/MsnXu
It was literally 25 messages into the discussion that I realized “data model” was being used in two distinct ways in the discussion. And even then it had to be pointed out by a participant who seemed to know one of the other people.
- I always mean “data model” in the ‘old’ way, in which the data model supports real-time business operations (or close thereto). In that world, you must design for integrity, which generally means ‘highly normalized’ in the relational sense.
- In the old-but-not-nearly-as-old world of OLAP, real-time operations and updates are not a concern, so de-normalization (and redundancy) are presumably acceptable. (I’ll leave that question to the experts.)
the problem with vocabulary – deeply buried assumptions that prevent you from hearing what you need to hear. From experience, I know the trap oh-so-well, but here I fell right into it myself.
What’s the answer to the question I posed for “data models” (of the kind I meant)? Focusing on the meaning and structure of business vocabulary, not data, as a core part of business analysis.
Note to self (a rule):
When you enter any discussion, be clear what you mean by the terms you use – even (and maybe especially) the ‘obvious’ ones.