Data Integration and Data Quality Management-My views

Data Integration and Data Quality Management is essential to make a DWBI solution successful. How does one measure if a DWBI solution is successful? Answer depends on who the subject is, how long the DWBI solution has been implemented. Usually within first few weeks after implementation, solution appears to be successful; but after Business Users starts using reports and analytics for decision making , then issues surface- " Metrics do not seem to be correct" Assuming solution adheres to initial business requirements and functional requirements, what explains occurrence of these issues? Source Data. Information in a Warehouse/mart, information &intelligence provided by reports and analytics will not be free from data quality and integration issues which exist at the source.

Two Options to solve these issues exist:
1. Develop business rules to massage & manipulate data to account for source data issues. This is an easier option as it involves dealing with limited stakeholders- DWBI sponsors, technology teams, Business users. But this option is a dangerous options: as it leads DWBI presenting different information from what operational systems' report. In the long run, this option limits usage of DWBI solution and makes DWBI solution a failure

2.Right option is to clean up data at Source/Opertional systems. This option requires commitment and buy-in from stakeholders across the enterprise. Cost and complexity involved with this options are much greater than those involved with option. I recommend a phased-approach to implement this option. Each phase should have definite goals to improve data in certain subject area(s). Analyze and identify various data ingtegration and data quality issues across subject areas. Prioritize issues based on cost,complexity and functional/business impact. In a larger context, this option is closely related to the topics I discussed in my earlier post.