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Data Modeling: 

1. Data modeling is the formalization and documentation of existing processes and events that occur during application software design and development. Data modeling techniques and tools capture and translate complex system designs into easily understood representations of the data flows and processes, creating a blueprint for construction or re-engineering.

2. A data model can be thought of as a diagram or flowchart that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, it's an important step and shouldn't be rushed. Well-documented models allow stake-holders to identify errors and make changes before any programming code has been written.

3. Data modelers often use multiple models to view the same data and ensure that all processes, entities, relationships and data flows have been identified. There are several different approaches to data modeling, including:

4. Conceptual Data Modeling - identifies the highest-level relationships between different entities.

5. Enterprise Data Modeling - similar to conceptual data modeling, but addresses the unique requirements of a specific business.

6. Logical Data Modeling - illustrates the specific entities, attributes and relationships involved in a business function. Serves as the basis for the creation of the physical data model.

7. Physical Data Modeling - represents an application and database-specific implementation of a logical data model.

8. See also: Unified Modeling Language (UML)

9. RELATED GLOSSARY TERMS: conformed dimension, star schema, dimension, snow flaking, predictive modeling