Understanding the Data warehouse life cycle
Despite warnings made by W.H. Inmon and others at the outset of the data warehousing movement in the early 1990s, data warehousing practice for the past decade at least has been prefaced on the assumption that, once in production, data warehouses and data marts were essentially static, from a design perspective, and that data warehouse change management practices were fundamentally no different than those of other kinds of production systems.
The pace of business change, combined with the ongoing search for competitive advantage through better decision-making in a climate characterized by commodity transactional systems and (increasingly) commodity decision support infrastructure, underscores the extent to which an organization’s understanding of, and control over, the entirety of the data warehousing lifecycle model can mean the difference between competitive differentiation on the one hand, and millions of dollars in cost sunk in brittle dead-end data warehousing infrastructure on the other.
In Building The Data Warehouse, published in 1991, W.H. Inmon made the observation that: The classical system development lifecycle (SDLC) does not work in the world of the DSS analyst. The SDLC assumes that requirements are known at the start of the design (or at least can be discovered). However, in the world of the DSS analyst, requirements are usually the last thing to be discovered in the DSS development lifecycle (p. 23).
At that time, Inmon advocated a data-driven approach to designing data warehouses, pointing out that (a) data warehouse analysts frequently understood their requirements, and the data available to them, only after they had the opportunity to perform various kinds of analysis on that data, and (b) the traditional waterfall oriented models of software development (particularly those enforced by high-end computer-aided software engineering, or CASE, tools) were unlikely to produce workable data warehousing environments.
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