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Principles and Rules Listing Page

Store as much detailed and granular data in data warehouse as possible

DW implementation is a big initiative. The use of data warehouse is unpredictable and may not be limited to summary level queries and for data analysis only.
 
This page of 'Principles and Rules' is linked to:  BI business intelligence end-to-end view, Data Warehousing, Data Analysis/OLAP, BI platform Tools Evaluation, Metadata Management, Core Data Management Tools,


As user start using DW, the application widens and people start looking for-

  • Drill down to the transaction level (for example if sales performance of a certain sales office is in red, which sales staff have not been performing) for a better root cause analysis.
  • Transactional level Reporting- As mentioned in why data warehouse, you will DW a good reference for generating your enterprise transaction reports.
  • Due to generally better and more sanitized data quality in data warehouse and also it being an offline platform, DW becomes a good source for enterprise reporting.

There are other more fundamental reasons for this tip:

  • Single reference point for all information needs: If you use DW as a single point reference, it will eliminate lot of reconciliation needs. This single reference point is possible, only when you have detailed data in DW.
  • Auditability of Data: Data in a DW may go through multiple stages of transformation. If you want to have the auditability of data. Detailed data provides a smooth audit trail, instead of summary data, whereby different records within that summary data could be having different audit trails.
  • Maximize the benefit of your ETL efforts: You would have done your extraction and transformation at the level of detailed data, before aggregating it for Data Warehouse. Therefore, if you have processed the detailed data already, it will be relatively a lesser effort to load the detailed data in DW. In other words, a good proportion of ETL efforts will remain the same irrespective of the level of detail you store in the DW.
  • Future Applications- While in the beginning you may be using the DW for purely summary data analytics, over time you will find many more applications as users taste the blood. Most of these applications (like enterprise reporting, data mining...) will be requiring data at detailed level. Prudent data warehouse architecture will include this future scenario in the scope and sizing of the DW.
  • Processing and storage capacity is less of a constraint now as it was few years back: This does not mean that infrastructure is not a challenge with DW. The size of data is also growing. However, we feel that cost per unit of storage and processing has fallen significantly. Moreover new technologies are emerging, which can handle data more smartly (like handling data explosion)

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Relevant Links to this page
Principles & Rules → Add extra buffer for ETL phase → Principles & Rules → Homework before interviews is must (Business Requirements Phase in Data Warehouse) → Principles & Rules → Excel is the competition, which should be challenged → Principles & Rules → Avoid Pure MOLAP → Practice Techniques → Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Consolidate Data-Marts → Practice Techniques → Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Licensing & Maintenance Contracts → Practice Techniques → Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Governance & Standards → Practice Techniques → Field Tips Series- Streamlining & reducing cost of Business Intelligence- Evaluate Open Source → Principles & Rules → Master Data Management- Making a Right Start → Practice Techniques → How to integrate stand-alone BI environments- Gradual Approach → Principles & Rules → Business owned applications are a reality- Manage it → Principles & Rules → New Data Standards- What about existing data and applications? → Principles & Rules → Handle Each Time-stamp in the Fact Table as a separate dimension → Principles & Rules → Keep Aggregates and Details data in different Fact tables → Principles & Rules → Some considerations for Infrastructure in Data Warehouse → Principles & Rules → For Core BI platform go for a single, established and robust player → Principles & Rules → Don't be guided only by the business requirements for your Business Intelligence → Practice Techniques → Using Synonyms and Views → 
 
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Relevant links to this page
Add extra buffer for ETL phase
Homework before interviews is must (Business Requirements Phase in Data Warehouse)
Excel is the competition, which should be challenged
Avoid Pure MOLAP
Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Consolidate Data-Marts
Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Licensing & Maintenance Contracts
Field Tips Series- Streamlining & Cost-Reduction in Business Intelligence- Governance & Standards
Field Tips Series- Streamlining & reducing cost of Business Intelligence- Evaluate Open Source
Master Data Management- Making a Right Start
How to integrate stand-alone BI environments- Gradual Approach
Business owned applications are a reality- Manage it
New Data Standards- What about existing data and applications?
Handle Each Time-stamp in the Fact Table as a separate dimension
Keep Aggregates and Details data in different Fact tables
Some considerations for Infrastructure in Data Warehouse
For Core BI platform go for a single, established and robust player
Don't be guided only by the business requirements for your Business Intelligence
Using Synonyms and Views
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