- The Business Case for a New Design Process
- Improving the Development Process
- Overview of Data Integration Modeling
- Conceptual Data Integration Models
- Logical Data Integration Models
- Physical Data Integration Models
- Tools for Developing Data Integration Models
- Industry-Based Data Integration Models
- End-of-Chapter Questions
Industry-Based Data Integration Models
To reduce risk and expedite design efforts in data warehousing projects, prebuilt data models for data warehousing have been developed by IBM, Oracle, Microsoft, and Teradata.
As the concept of data integration modeling has matured, prebuilt data integration models are being developed in support of those industry data warehouse data models.
Prebuilt data integration models use the industry data warehouse models as the targets and known commercial source systems for extracts. Having industry-based source systems and targets, it is easy to develop data integration models with prebuilt source-to-target mappings. For example, in banking, there are common source systems, such as the following:
- Commercial and retail loan systems
- Demand deposit systems
- Enterprise resource systems such as SAP and Oracle
These known applications can be premapped to the industry-based data warehouse data models. Based on actual project experience, the use of industry-based data integration models can significantly cut the time and cost of a data integration project. An example of an industry-based data integration model is illustrated in Figure 3.17.
Figure 3.17 Industry-based data integration model example
In the preceding example, the industry data integration model provides the following:
- Prebuilt extract processes from the customer, retail loan, and commercial loan systems
- Prebuilt data quality processes based on known data quality requirements in the target data model
- Prebuilt load processes based on the target data model subject areas
Starting with existing designs based on a known data integration architecture, source systems, and target data models, provides a framework for accelerating the development of a data integration application.