Continued from part 1.
Conversely, an 'Individual' can purchase the service. This 'Individual's' company becomes, in turn, 'Company' in the operational system, so two categories of 'Company' and 'Individual' exist in the operational system -- those who pay and those who don't pay for the service. This type of controversy between the business definition and the nature of existing data can make the deadline of a modeler Friday the 13th. To avoid that, guidelines for a clever modeler include:
- Decompose the business objects into the smallest possible attributes.
- Group the common attributes together.
- Introduce more generic entities into the model that can collapse all other objects from the perspective of existing data.
- Implement proper flagging to identify the category of the respective record.
- Define the top-level dimensions for the common attributes.
- Attach the facts like 'Sale', 'Order' or 'Revenue' to the generic entity which rolls up to the common attributes which again, rolls up the highest level of the dimension.
- Capture the snapshots of all slowly changing data from one definition to another like 'Contact' to 'Customer' and 'Customer' to 'Client'. Remember, operational system will not provide this directly. What the ETL needs to do is to capture all slowly changing dimensions and
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Hannah Smalltree, Editorial Director- put respective flags on the data at the time of cleansing, massage or reconciliation.
- Define rigid business rules for the ETL to classify different category in the generic object (Business users are the perfect guide to synthesize the definitions) and implement them in transformation rules.
- Design a repository to collect all related metadata attributes of respective categories and give their access to the OLAP Cubes.
- Design proper summary tables or implement data marts to categorize a generic entity and attach it to the cube to minimize the object analysis for the cubes.
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This was first published in March 2002
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