If a central bank is to set up a data warehouse, what all factors should it look into?
David Marco's Answer: Wow! I could write for hours on this topic. I'll try to keep my answer as short as possible. First, the overriding reason many decision support projects fail is not that the projects were technically unfeasible. On the contrary, much of the technological challenges of data warehousing have proven answers. The most common cause for failure is that the warehouses didn't meet the business objectives of the organization. Warehouses that don't satisfy the business user's needs are not accessed and eventually die.
Second, any large-scale project - whether it's a decision support system (DSS) or if you're implementing that hot new customer relationship management system - needs executive management on board. Moreover, their involvement is imperative in breaking down the barriers and the "ivory towers" in all of our companies. Their position allows them the ability to rally the various departments within a corporation behind the project. Any substantial project lacking executive management participation has a high probability of failure.
Third, don't try to build a "do-it- all", "be-all" decision support system on the first development phase. Instead build your data warehouse in an iterative fashion, providing value during each stage. DSS projects stretch an organization in ways unlike that of other IT projects. From a political perspective, an enterprise data warehouse requires consent and commitment from all of the key departments within a corporation. In addition, the learning curve of DSS project team is seldom understood. There will be a new and dizzying array of software tools (transformation, access, meta data, data cleansing and data mining) that will require tool- specific training.
By adding massive amounts of data into the equation the points of failure increase significantly. Moreover, large volumes of data will push the envelope of the RDBMS, middleware, hardware and vendor software.
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