Missing data, gaps in data, lost time, and mounting costs are just a few of the pitfalls that you may encounter while building a data warehouse or data mart. Implementing simple prototyping techniques is a sensible way to mitigate the adverse effects of unforeseen problems. The development cycle of a typical data warehouse or data mart includes various stages such as data requirement analysis, modeling, extraction, transformation, loading, report development, and testing. Because of the significant time lapse between the requirement analysis phase and final testing of the data warehouse, extensive gaps in data often go undetected until the beta-testing phase of the data warehouse. Discovering missing data at a later stage of the project often comes with a significant price tag in terms of money and lost time.
Prototypes illustrate the power and capabilities of data warehousing to the user community.
They are especially helpful to companies that are attempting to build their first data warehouse. For example, a medium-sized financial institution could have as many as 5,000 or more data elements that are used throughout their enterprise. Unless your company is a startup, it is very likely that your source data is buried in legacy systems, with little or no documentation. With these conditions and challenging deadlines, even the most experienced data warehousing professionals are likely to miss addressing some of the critical functional and data issues in their logical and physical designs. Seven Classic Prototyping Scenarios Make enterprisewide data architecture decisions.