Dimension Attributes as NULL

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Dimension Attributes as NULL
You should generally not leave dimension attributes as NULL.

This page of 'Principles and Rules' is linked to: Data Warehousing, Data Analysis/OLAP, BI platform Tools Evaluation, BI business intelligence end-to-end view,

This with with reference to other field tips (Handling facts as Nulls and Handling NULL fact foreign keys). In case you have a situation where the dimension attributes as NULL, you should not leave them like that. NULL creates confusion for the users as they appear in reporting and some of the OLAP systems have different ways to handle these NULLs. The best way is to provide a user-friendly string or description. For example "Not-available", "Not-Applicable", "Not-provided".

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Principles & Rules → Data Warehouse application is not limited to Analytics → Principles & Rules → Store as much detailed and granular data in data warehouse as possible → Principles & Rules → Data Normalization is not the best approach in Dimensional modeling → Principles & Rules → Keep the same names and definitions for all data elements → Principles & Rules → You cannot have a super-flexible Data warehouse → Principles & Rules → Dimensional models can be extensible and scalable → Principles & Rules → Data Marts should be ideally based upon a business process and not on a department. → Principles & Rules → Business Intelligence competency groups should be well-linked with business → Practice Techniques → Aggregation Queries on slowly changing Dimensions → Practice Techniques → Documenting your data-integration system → Principles & Rules → For a Data Warehouse/Data-Mart solution, analyze well, but be decisive → Principles & Rules → Maintain a trail of the key dimensional elements from source system to loaded → Principles & Rules → Conformed dimensions are must for cross-drilling → Practice Techniques → Checksum Approach for identifying the changed records from source systems → Principles & Rules → Dimensional model has to be aligned to the Entity-Relationship → Principles & Rules → Always Use Conformed Dimensions → Principles & Rules → You may not be a able to have a perfect ETL → Practice Techniques → Handling Sparse Dimensional tables → Principles & Rules → Do not separate the parent and child line item data → Practice Techniques → Managing time-stamps across multiple time-zones → Practice Techniques → Recording events in multiple currencies → Practice Techniques → Handle different units of measure in the same fact table → Principles & Rules → Handling of Null foreign Keys in fact tables → Principles & Rules → Dimension Attributes as NULL → Principles & Rules → Don't rely too much on Meta Data Tools to enforce Business Intelligence → Principles & Rules → Don't wait for universal models for Data Marting →

Relevant links to this page

Data Warehouse application is not limited to Analytics
Store as much detailed and granular data in data warehouse as possible
Data Normalization is not the best approach in Dimensional modeling
Keep the same names and definitions for all data elements
You cannot have a super-flexible Data warehouse
Dimensional models can be extensible and scalable
Data Marts should be ideally based upon a business process and not on a department.
Business Intelligence competency groups should be well-linked with business
Aggregation Queries on slowly changing Dimensions
Documenting your data-integration system
For a Data Warehouse/Data-Mart solution, analyze well, but be decisive
Maintain a trail of the key dimensional elements from source system to loaded
Conformed dimensions are must for cross-drilling
Checksum Approach for identifying the changed records from source systems
Dimensional model has to be aligned to the Entity-Relationship
Always Use Conformed Dimensions
You may not be a able to have a perfect ETL
Handling Sparse Dimensional tables
Do not separate the parent and child line item data
Managing time-stamps across multiple time-zones
Recording events in multiple currencies
Handle different units of measure in the same fact table
Handling of Null foreign Keys in fact tables
Dimension Attributes as NULL
Don't rely too much on Meta Data Tools to enforce Business Intelligence
Don't wait for universal models for Data Marting

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