What are the disadvantages of the Canonical Data Model?

Question

I can find a lot of information in favor of using the Canonical Model, but I want to know about some of its disadvantages or weaknesses, and possibly gain some knowledge on the various approaches of creating such models.

Answer 1

Disadvantages

1. May sometimes require more customization effort, than building a model from scratch
2. May create difference of opinion arguments and potential road blocks from your own experienced data modelers
3. May reduce competitive advantage of business intelligence and analytics (since competitors may be using the same model)
4. Goes against “agile” BI principles that call for small, quick, tangible deliverables
5. Goes against top down performance management design and modeling best practices, where one does not start with a logical data model but rather:
   • Defines departmental, line of business strategies
   • Links goals and objectives needed to fulfill these strategies
   • Defines metrics needed to measure the progress against goals and objectives
   • Defines strategic, tactical and operational decisions that need to be made based on metrics
   • Then, and only then defines logical model needed to support the metrics and decisions

A practical approach for creating a model:

1. Start with creating reference tables (standard domain definitions valuta, countries, marital status, cities etc.)
2. If there are more that 2 consumers then standardize the common concepts (customer, address)
3. If available use or refer to public standards (ISO, ontologies e.g. Dublin Core) Explicitly assign ownership for the model.