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

Excel is the competition, which should be challenged

One of the most common questions you will phase is that "why can't we do the same thing in excel? Just import the data and create the pivots and bingo..". Be ready with the arguments of Data Integrity, Historical Data, Integration across the system, common definitions etc..
 
This page of 'Principles and Rules' is linked to:  Data Analysis/OLAP, BI platform Tools Evaluation, Data Warehousing, BI business intelligence end-to-end view,



For any Data Management or business intelligence (aka information management) work of any significance, excel is not the right tool (unless it is used just for front end and is not the repository of Data). The reasons are as follows:

  • Data Integrity: You can send the excel files via email, they can be changes by different users, there can be separate version floating across in the system, they don't have the way to synch up with the data in company databases. For example the customer master in an excel file could be totally out of synch with the customer master in your ERP systems.
  • Its difficult to refer to common meta-data to manage data on excel.
  • The scalability of excel.

These are few of may reasons that excel should not be considered as a tool to do any initiative of significance.


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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 → 
 
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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
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