My book describes how the convergence of new web-based architectures, advanced object-oriented methodologies, and powerful computing architectures can create business results for your organization. However, even with these enabling technologies, the construction of the data warehouse remains a challenging undertaking. Success requires both a capable team and a group of users willing to change their daily activities. (For the Oracle - Business Objects users, this text is a must-have, see the next-to last paragraphs of my comments).
Success for your organization means improving the quality of what your team does. To improve your project's productivity and success, I describe how advanced visualization and modeling capabilities from object oriented analysis design components of a data warehouse. In addition, I use the unified modeling language (UML) to detail the steps of the of the data warehouse method for both data modeling and data acquisition. In my chapter on Design, I show how UML can address many dimensional modeling issues that were impossible to solve with the entity relationship diagram (ERD). The DWM presents a complete solution that utilizes the Oracle 8.0 RDBMS as the data source. The DDL and sample data for the problem is included on the CD.
The DWM improves the success of the project, by incrementally breaking the line-of-business into cycles that implement business-models. This technique incorporates scaleable design techniques, including data partitions, and delivers short-term business results and insures that the project cycle built today will be reusable by those next waiting to roll out of the factory. By building focused, business-model based data marts at three-six month intervals; the DWM reduces the time required to deliver business results for your organization.
The data warehouse project can be very risky. According to the META group, after one year more than 50% of data warehouse projects have failed to achieve their objectives. Another study of large corporations attempting to construct large-scale data warehouses reports more than 80% of all data warehouse projects fail to meet organizational objectives, with a significant portion in complete failure. The process of acquiring data from operational systems, transforming it, and loading it into the data warehouse can be a fundamental cause of a project's failure. The prevalent use of pre-defined star schemas, or 'by the book' solutions, may delude the project team into thinking that the organization's operational systems will (easily) support the data model. I have often found that some project teams don't even attempt to load operational data until late in the project. Until this loading takes place, the project team cannot truly evaluate how well the business rules model of the operational system matches the design.
In the DWM, data acquisition is a critical component of the process. Very little has been written on this topic. Therefore -- at each step in the process, I show how early data prototypes and extracts from operational systems are critical to the success of the organization. The team that applies the object-oriented analysis in this book will improve the efficiency and effectiveness of the integration between the data warehouse and the operational systems. This method eliminates serious project risk by moving operational data from source system(s) very early in the project cycle.
Eventually, every data warehouse manager must seek out ways of improving the performance of the mature environment. In latter chapters of the text, I develop the concept of aggregate management. Through this approach, pre-summarized subsets of fact tables are precisely configured to dramatically improve the performance of the managed query environment. The CD included with this text contains executable source for an 'Aggregate Wizard'. This program merges the semantic and CASE repositories to provide an important service to the users, a highly performing, highly available data warehouse. It specifically works with the Oracle and Business Objects environment and utilizes data structures that can be incorporated into the universes with the @aggregate_aware function. I have included the source of the processes and data schema so that readers may develop their own aggregate solutions.
One of the key reasons that I wrote this text is that I have observed an evolution in the use the CASE, semantic, and administration repositories. To me, this suggests new types of methodologies