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by Burleson Consulting

The Data Warehouse Development Life Cycle

Warehouse Project Management   When embarking on a data warehousing project, many pitfalls can cripple the project. Characteristics of successful data warehouse projects generally include the following aspects:

* Clear business justification for the project--Measurable benefits must be defined for a warehouse project (e.g., sales will increase by 10 percent, customer retention will increase by 15 percent). Warehouses are expensive, and the project must be able to measure the benefits.

* Staff is properly trained--Warehousing involves many new technologies, including SMP, MPP, and MDDB. The staff must be trained and comfortable with the new tools.

* Insuring data quality and consistency--Warehouses deal with historical data from a variety of sources, so care must be taken to create a metadata manager that ensures common data definitions and records changes of historical data definitions.

* Insuring subject privacy--Gathering data from many sources can lead to privacy violations. A good example of privacy violation is the hotel chain that targeted frequent hotel customers and sent a frequent-user coupon to their home addresses. Some spouses intercepted these mailings, leading to numerous divorces.

* Allow the warehouse to start small and evolve--Some projects fail by defining too broad of a scope for the project. Successful projects consider their first effort as a prototype and continue to evolve from that point.

* Ensure intimate end-user involvement--Data warehouses cannot be developed in a vacuum. The system must be flexible to address changing end-user requirements, and the end-users must understand the architecture so they are aware of the limitations of their warehouse.

* Properly plan the infrastructure--A new infrastructure must be designed to handle communications among data sources. Parallel computers must be evaluated and installed, and staff must be appropriately educated.

* Perform proper data modeling and stress testing--The data model must be validated and stress tested so that the finished system performs at acceptable levels. A model that works great at 10 GB may not function as the warehouse grows to 100 GB.

* Choose the right tools--Many projects are led astray because of vendor hype. Unfortunately, many vendors inappropriately label their products as “warehouse” applications, or they exaggerate the functionality of their tools.

This is an excerpt from "High Performance Data Warehousing". To learn more about Oracle, try "Oracle Tuning: The Definitive Reference", by Donald K. Burleson.  You can buy it direct from the publisher at 30% off here:


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