Don't rely too much on Meta Data Tools to enforce Business Intelligence

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Don't rely too much on Meta Data Tools to enforce Business Intelligence
Meta Data Tools in today's world have great capabilities , however it is safer to have the business rules enforced through the dimensional models and databases within OLAP, Staging and Data Warehouse.

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, Metadata Management, Core Data Management Tools,

The reason is that a meta-data tool may not always be used to get the information. Some examples are:

You may shift to a new meta-data tool or having different departments using different meta-data tool.

Technical experts may directly query on the database.

You company might be integrated with some other company, leading to a change in the meta-data tool.

This does not mean that meta-data tool should not be used, and of-course not all the business rules, which can be enforced by Meta-Data (Semantic layer and navigation to a certain extent..), we should try have an optimum set of business rule ingrained within the database/OLAP itself, to ensure that business get a consistent and reliable information irrespective of the method of access.

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Relevant Links to this page

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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