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Entry criteria to start DW project

Should one wait for the operational systems to stabilize or having a minimum thresh-hold of data quality before starting on the data warehouse project?
 
This page of 'Ask a question' is linked to:  Data Quality, Data Warehousing,

If you had a recent major implementation with the Source Systems to Data Warehouse, it will make sense to wait for few months. This is because there is a natural cycle for system to settle down. However if there is routine state of instability in the source systems, one should embark upon the data warehouse project. This will lead to more painful data extraction and transformation process. The advantage is that it will only help identify the issues in the source systems as well as providing a cleaner information source for your enterprise reporting.

Some possible advantages of starting a data warehouse initiative in an unhealthy source system landscape:

  • A good proportion of the effort you will spend in ETL (data profiling of the source system, extraction and transformation routines) will help you to address the data quality issues of the source system. You may identify some quick fixes to address data quality in source systems.
  • You will create a more reliable source for business reporting, as Data warehouse data will be cleaner. Your data warehouse initiative itself might have been triggered due to the instable source systems. This will in future move your reporting to data warehouse, as it being a more reliable source. Also when you embark upon the data quality program for the source systems, you will be more educated on the root causes and possible solutions.
  • As you work on ETL, you will get an opportunity to the level of mess and reasons for bad data quality (or instability) in the source systems. This will accelerate management focus on the issues, and prompt more funding in that direction.

Here are the recommendations to get the advantage out of above-stated approach

  • Don't over commit and start small. If your source systems are relatively unstable, you may have risk of failing in building robust ETL. We recommend to start small with more stable systems.
  • Ensure active engagement with people who 'know the secrets'. Have a greater connect and linkage with IT and Business specialists (more than what you would have done with the stable systems). These folks would be able to guide you on the weak areas, and will also share the 'bandages and crutches' which have been putting on the system.
  • Ask for more than estimated time for ETL phase .

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Relevant Links to this page
Expert's Answers → Prime Purpose of a Data Warehouse → Expert's Answers → Online data feed to Data Warehouse. → Expert's Answers → Data Warehouse- Big Bang or Incremental → Expert's Answers → Dimensional model vs Relational model → Expert's Answers → Interview sequence for DW business requirements → Expert's Answers → Pre-configured reports- Do they work? → Expert's Answers → Business Intelligence vs Business Performance → Expert's Answers → Data Warehouse vs Business Intelligence → Expert's Answers → Refresh frequency of a Data Warehouse → Expert's Answers → EAI vs Data Warehouse → Expert's Answers → Sponsorship for Data Quality. → Expert's Answers → Ownership of Data Quality Initiative → Expert's Answers → Excel export to Data Warehouse → Expert's Answers → Captive ERP reporting capability → Expert's Answers → CRM and Data Warehouse → Expert's Answers → Starting A Data Quality Program → Expert's Answers → Source system re-writing in parallel to Data Warehouse → Expert's Answers → Data Profiling tool for Data Quality → Expert's Answers → Statistical sampling for Data Quality. → Expert's Answers → Simultaneous launch of source system and Data Warehouse → Expert's Answers → Security Matrix of a Data Warehouse → Expert's Answers → Data Quality program prioritization. → Expert's Answers → ETL phase taking too long → Expert's Answers → Should Data Warehouse wait for Meta-Data initiative → Expert's Answers → Mismatch in Source vs Data Warehouse reporting. → Expert's Answers → Data Warehouse vs Data Mart vs Data Mining → Expert's Answers → Data Quality Assurance vs. Risk Assessment → Expert's Answers → Data Quality Business Ownership in high-transition environment → Expert's Answers → Including informal and small systems in your Data Quality scope → Expert's Answers → When to use what level of detail for DQ assurance tracking? → Expert's Answers → Level of usage of Data Quality Practice Tool-Kit → Expert's Answers → Evolution path for Data Quality Practice Tool-Kit → Expert's Answers → Data Management Standards for Data Quality → Expert's Answers → Data Quality Practice Kit in work-flow and collaboration → Expert's Answers → Data Quality Policy- Level of Coverage → 
 
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Relevant links to this page
Prime Purpose of a Data Warehouse
Online data feed to Data Warehouse.
Data Warehouse- Big Bang or Incremental
Dimensional model vs Relational model
Interview sequence for DW business requirements
Pre-configured reports- Do they work?
Business Intelligence vs Business Performance
Data Warehouse vs Business Intelligence
Refresh frequency of a Data Warehouse
EAI vs Data Warehouse
Sponsorship for Data Quality.
Ownership of Data Quality Initiative
Excel export to Data Warehouse
Captive ERP reporting capability
CRM and Data Warehouse
Starting A Data Quality Program
Source system re-writing in parallel to Data Warehouse
Data Profiling tool for Data Quality
Statistical sampling for Data Quality.
Simultaneous launch of source system and Data Warehouse
Security Matrix of a Data Warehouse
Data Quality program prioritization.
ETL phase taking too long
Should Data Warehouse wait for Meta-Data initiative
Mismatch in Source vs Data Warehouse reporting.
Data Warehouse vs Data Mart vs Data Mining
Data Quality Assurance vs. Risk Assessment
Data Quality Business Ownership in high-transition environment
Including informal and small systems in your Data Quality scope
When to use what level of detail for DQ assurance tracking?
Level of usage of Data Quality Practice Tool-Kit
Evolution path for Data Quality Practice Tool-Kit
Data Management Standards for Data Quality
Data Quality Practice Kit in work-flow and collaboration
Data Quality Policy- Level of Coverage
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