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Establishing 'Making it Happen' as a 'Formal & Predictable' Discipline
Principles and Rules Listing Page

Homework before interviews is must (Business Requirements Phase in Data Warehouse)

Most of the business themes, challenges, issues and information requirements are available in the PPTs, Minutes of the meetings, business plan submissions etc.
 
This page of 'Principles and Rules' is linked to:  Data Analysis/OLAP, BI platform Tools Evaluation, Data Warehousing,



One can go through many references (like business plans, SWOT, strategy blue-print, key management presentations, goals-sheets of key personnel) and internalize them before starting interviews of business requirement phase for a BI initiative. or else one runs the risk of lack of control as well as responses driven by immediate crises instead of medium to long term perspective.

This is a make or break factor in the business requirement phase in the Data warehouse initiative.

As a prudent practice, you should ask the business users open ended questions, like "What are the top 5 business questions, which you want to get answered?". If you give them choices out of which to select, you may be influencing their thought process, which is not advisable.

However at the same time, you should be able to ask intelligence questions to probe or validate the answers given by the business users. For example, if a business user tells a business priority, which is not in line with the strategy plan document published two months back, you should step in a question this ms-alignment.

The biggest advantage of doing your home-work is that you would be able to relate to the business users, as they will realize that you understand their world and context. This will encourage them to open-up and provide you more insights. They will not like to spend time to educate you on their business.


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Relevant Links to this page
TOPIC - Data Warehouse Business Requirements → Data Warehouse Requirements Assessment → 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 → 
 
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Relevant links to this page
Data Warehouse Business Requirements
Data Warehouse Requirements Assessment
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
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