This month, we’ll continue our review of the data warehouse analysis and design process for decision support systems. Unlike traditional waterfall systems development processes, data warehouse design is iterative – not only do we rapidly go through the analysis-to-build process, but the transition from development to production is different as well. The important point to remember is that we are still building a production system for business analysis. This means that even though we are moving through the analysis-to-build process in a rapid iterative form, we still must comply with all of the due diligence processes required to properly certify the data warehouse for production. How should we do this? To complete the process of DW design and production readiness, we usually can delegate the work to two project teams. The first deals with the design of the data warehouse database, extraction and presentation layer; the second will address technical infrastructure, production control, testing and certification, end-user training, and production handover and sign off. The aggressive time frames inherent in today’s design of operational and analytic information systems dictate that parallel work streams become the norm. The infrastructure team needs to get the development environment set up for the analysis team as an initial activity to be followed by the definition of the unit and acceptance testing environment, production preparation and hand-over of documentation and training. Once the development environment is defined, the analysis team can proceed with the process of gathering the user requirements and the initial design of the following:

While all this is occurring, the infrastructure team proceeds with the design and deployment of the following:

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