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Mike Ferguson

Welcome to my blog on the UK Business Intelligence Network. I hope to help you stay in touch with hot topics and reality on the ground in the UK and European business intelligence markets and to provide content, opinion and expertise on business intelligence (BI) and its related technologies. I also would relish it if you too can share your own valuable experiences. Let's hear what's going on in BI in the UK.

About the author >

Mike Ferguson is Managing Director of Intelligent Business Strategies Limited, a leading information technology analyst and consulting company. As lead analyst and consultant, he specializes in enterprise business intelligence, enterprise business integration, and enterprise portals. He can be contacted at +44 1625 520700 or via e-mail at

Following on from my last blog on data federation, the next data federation pattern I would like to discuss is a Data Warehouse Virtual Data Mart pattern. This is as follows:

Pattern Description

This pattern uses data virtualization to create one or more virtual data marts on top of a BI system thereby providing multiple summarised views of detailed historical data in a data warehouse. Different groups of users can then run ad hoc reports and analyses on these virtual data marts without interfering with each others' analytical activity.


Pattern Diagram


Virtual DM Pattern.JPG


Pattern Example Use Case

Multiple 'power user' business analysts in the risk management department of a bank often need their own analytical environment to conduct specific in-depth analyses in order to create the best scoring and predictive models. This pattern facilitates the creation of multiple virtual data marts without the need to hold data in many different data stores


Reasons For Using It

Reduces the proliferation of data marts and also prevents inadvertent 'personal' ETL development by power users who have a tendency to want to extract their own data to create their own data marts. It is often the case that each power user wants a detailed subset of data from a data warehouse that overlaps with the data subsets required by other power users. This pattern avoids inadvertent inconsistent ETL processing on extracts of the same data by each and every power user. It also avoids the duplication of the same data in every data mart, improved power user business analyst productivity, reduces the time to create data marts and reduces the total cost of ownership.  




Posted November 27, 2009 7:01 AM
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