Picture this: You’re the tech lead on a large ETL project, integrating data from 17 suppliers and five internal systems for a logistics management application. The system will enable planners to optimize the supply chain with integrated order, shipment and production data. The team is finalizing design, having already defined business requirements, identified business data elements, completed database design and assembled Excel source-to-target mappings for all 107 interfaces. Your project manager stops by to tell you that a new supply director wants to change five of your key requirements, and that the database administrators redesigned one section of the model to meet new standards. She needs an impact assessment before the steering committee meeting at 10:00 a.m. tomorrow morning.
Once development starts, data integration projects are served well by metadata tools. Major ETL vendors like Informatica, IBM WebSphere DataStage and others include metadata solutions that document mappings and transformations, enabling impact analysis in the event of interface changes, database design changes and data quality problems. However, metadata associated with development tools only kicks in when development starts. A significant part of data integration effort happens before the virtual pen meets paper to build ETL maps. Development can only begin after the team defines requirements, designs the database and maps source data elements to their targets.
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