Enabler 5 in my list for Business Intelligence Utopia are the ubiquitous, hard-working “Data Models”. Data Model is the heart of any software system and at a fundamental level provides placeholders for data elements to reside.

Business Intelligence systems with all its paraphernalia – Data Warehouses, Marts, Analytical & Mining systems etc. typically deals with the largest volume of data in any enterprise and hence data models are highly venerated in the Data Warehousing world.

At a high level, a good Data Warehouse data model has the following goals: (Corollary – If you are looking for a data modeler look for the following traits)

1) Understand the business domain of the organization

2) Understand at a granular level the data generated by the business processes

3) Realize that business data is an ever-changing commodity – So the placeholder provided by the data model should be relevant not only for the present but also for the future

4) Can be described at a conceptual and logical level to all relevant stakeholders

5) Should allow for non-complicated conversion to the physical world of databases or data repositories that is manipulated by software systems

Extensible Data models deal with all the 5 points mentioned above and more specifically has future-proofing as one of its stated goals. Such extensible models are also “consumption agnostic”, i.e. – it provides for comparable levels of performance irrespective of the way data is being consumed.

It is important for BI practitioners to understand the goals of their data models before embarking to use specific techniques for implementation. Entity-Relationship & Dimensional modeling (http://www.rkimball.com) has been the lingua-franca of BI data modelers operating at the conceptual and logical levels. Newer techniques like Data Vault (http://www.danlinstedt.com/) also provides some interesting thoughts in building better logical models for Data Warehouses.

At the physical implementation level, relational databases still form the backbone of the BI infrastructure, supplemented by multi-dimensional data stores. Even in the relational world, traditionally dominated by row-major relational vendors like Oracle, SQL Server etc. there are column-major relational databases of the likes of Sybase IQ with claims of being built ground-up for data warehousing.

In this article on column major databases – http://www.databasecolumn.com/2007/09/one-size-fits-all.html, there is reference to a new DW specific database architecture called Vertica. It makes for a fascinating read – http://www.vertica.com/datawarehousing. The physical layer is also seeing a lot of action with the entry of data warehousing appliance vendors like Netezza, Datallegro etc. (http://www.dmreview.com/article_sub.cfm?articleId=1009168).

The intent of this post can be summed up as:

a) Understand the goals of building data models for your enterprise – Make it extensible and future proof

b) Know the current techniques that help envisage and build data models

c) Be on the look-out for new developments in the data modeling and database world – There is lot of interesting action happening in this area right now.

Extensible data models combined with the right technique for implementing them, lists as Enabler 5 in the “Power of Ten” for Business Intelligence Utopia.

Posted by Karthikeyan Sankaran
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October 15th, 2007

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