Research IT Application Development


Data Modeling

  • Our data-centric view enables our ability to meet Business Reporting Requirements efficiently and accurately.  
  • For operational data, we advocate a fully normalized data structure and Entity-Relationship modeling.  
  • Data Warehouse techniques can be used to provide the basis for flexible and consistent reporting  strategies. Aligning data along business process dimensions, including de-normalization of hierarchical dimensions, can lead to reporting simplicity and efficiency. Use of the data-bus architecture allows for incremental development of the Data Warehouse within a controlled framework.
  • One of the keys to a successful Data Warehousing initiative is less a technical  issue than a management one.  It is essential that there is management  support at a high enough level in the organization to achieve consensus on what the basic dimensions are and what they represent.  If there are  department heads that have different definitions of the basic entitiesof the enterprise, often because their operational systems evolved independently,  it can require higher level direction and support to broker an agreement.
  • For Operational or Data Warehousing, we prepare the logical data model using Erwin.  
  • A good data model is an excellent  starting point for developing XML schemas as standards for  communication between processing systems.  
  • UML can be an effective tool for capturing user requirements at the beginning of system development, and serve as documentation of system capabilities for maintenance and testing.