Pros and cons of using a vendor provided analytical data model in your BI implementation

The following question comes from many of our clients: what are some of the advantages and risks of implementing a vendor provided analytical logical data model at the start of any Business Intelligence, Data Warehousing or other Information Management initiatives? Some quick thoughts on pros and cons:


  • Leverage vendor knowledge from prior experience and other customers
  • May fill in the gaps in enterprise domain knowledge
  • Best if your IT dept does not have experienced data modelers 
  • May sometimes serve as a project, initiative, solution accelerator
  • May sometimes break through a stalemate between stakeholders failing to agree on metrics, definitions



  • May sometimes require more customization effort, than building a model from scratch
  • May create difference of opinion arguments and potential road blocks from your own experienced data modelers
  • May reduce competitive advantage of business intelligence and analytics (since competitors may be using the same model)
  • Goes against “agile” BI principles that call for small, quick, tangible deliverables
  • 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 


Thoughts, comments?


Canonical data models for integration too

Hi Boris,

I am currently working on a canonical data model for a government client. They already have one based on a vendor provided data model but it has suffered from a number of problems. Certainly the pros (and the reasons that they used the vendor model in the first place) are those that you list. The cons are similar to those for a BI logical model, however there are a few more:
Lack of documentation or knowledge of the data model means that it is poorly used/maintained/extended.
Isn't connected to the business view of information.
Doesn't fit the specific concerns or needs of this enterprise very well.
My response - similar to John's above is to create a high level business information model (a conceptual model) that captures the business view of the information that the enterprise uses which can then be traced down to the logical data models. In all cases however, the documentation on the lower level data model must be good and include how to govern the model, how to extend it, patterns for extending etc. That way the model can then be maintained going forward.


re: Pros and cons of using a vendor provided analytical data mo

You've captured a good list of the pros and cons of using a model-driven approach. We advocate starting with a conceptual business model, which is an even higher level of abstraction. This approach helps avoid running into some of the disadvantages you've listed, especially those that relate to having a fixed data model. For example, customers can start from scratch or with a pre-defined business model, quickly customize it for their specific business needs, and then generate the physical model. This separation avoids having the "same" model as a competitor because yours is set up for your specific needs. And because the business model is easy to change, you don't lose any agility; in fact that aspect is enhanced. Finally, while you may still have difference of opinion arguments, our customers tell us they end up with a business model that is jointly shared between the IT and business people involved, and ultimately this ensures clarity around the scope, the content and the output from the project. Setting expectations is a huge win for both sides!

There's some really good independent and video-based educational material on business modeling put together by Cliff Longman at Adaptable Data. If your readers are looking to hear about how to use data models to communicate effectively with business people, it is a great resource. Check it out at