The challenge with sourcing data from operational systems and integrating data from multiple disparate source systems is formidable, and often laden with technical hurdles. The business users have struggled with this challenge for years and the natural reaction is to include as much scope of source data in the BI project as possible. However, BI implementers learned that you can't try and deliver enterprise BI in one large project as users weren't willing to wait that long for the solution and what was delivered had no longer met their requirements. In response to this challenge, enterprise BI projects are now divided into smaller iterations, which deliver a subset of user requirements in shorter timeframes. While it initially helps solve the challenge of long delivery cycles, most organizations discover that each subsequent iteration add increased maintenance overhead. This increased overhead is caused by:
- Non-scalable data warehouse and application architectures.
- Variation in DW data models, data integration mapping designs and semantic layers.
- Metadata duplicated in multiple software repositories.
- Disparate scheduling.
- Non-reusable components.
- Multiple security architectures.
- “Plug and play” stovepipe solutions.
- Non-existent data/systems governance.
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