Моделирование бизнес-процессов / Моделирование бизнес-процессов / ERwin_Insider_2

I have also found databases that are transaction driven, not data driven. This means that the database is structured to store information on the basis of each transaction completed by the system rather than logically and to at least some degree of normalization. This can make data analysis and extraction of data for use in other applications very difficult without a full understanding of the functional design of the application.

It's not going to get any easier. As vendors move to the object relational paradigm and begin to incorporate arrays, nested tables, methods and the like, data base normalization and hence the ability to understand business rules from database structure will become even cloudier.

Just as I was collecting my thoughts for these lines, I found a recent column 11 by the venerable data warehouse guru Ralph Kimball, bemoaning the fact that vendors (in this case ERP vendors have build difficult to penetrate walls around their data and business processes. These walls, says Kimball, are a result of a number of things including "ERP systems have been far more concerned with transaction processing than decision support….back room culture that doesn't validate the needs of front room end users."

"ERP systems' primary database schemas are absurdly complex…" a complexity compounded but the lack of any consideration for the rules of normalization. Kimball concludes with among other things a call for a seamless transitions and open systems, with out which the importance of ERP's could be reduced.

We seen time and time again, open platforms have been the most successful. To wit, Microsoft. Environments protected, either naturally or contrived because of complexity, eventually fall by the way side. So here's my pitch. As we move into the era of common frameworks, and open systems , vendors open up your systems. The best place to start is with more normalized databases, and a logical data model included with the application documentation.

11 Data Webhouse in Intelligent Enterprise June 26, 2000