Data Modeling

1. INTRODUCTION TO LOGICAL DATA MODELING
· Importance of logical data modeling in requirements
· When to use logical data models
· Relationship between logical and physical data model
· Elements of a logical data model
· Read a high-level data model
· Data model prerequisites
· Data model sources of information
· Developing a logical data model

2. PROJECT CONTEXT AND DRIVERS
· Importance of well-defined solution scope
· Functional decomposition diagram
· Context-level data flow diagram
· Sources of requirements
o Functional decomposition diagrams
o Data flow diagrams
o Use case models
o Workflow models
o Business rules
o State diagrams
o Class diagrams
o Other documentation

· Types of modeling projects
o Transactional business systems
o Business intelligence and data warehousing systems
o Integration and consolidation of existing systems
o Maintenance of existing systems
o Enterprise analysis
o Commercial off-the-shelf application

3. CONCEPTUAL DATA MODELING
· Discovering entities
· Defining entities
· Documenting an entity
· Identifying attributes
· Distinguishing between entities and attributes

4. CONCEPTUAL DATA MODELING-IDENTIFYING RELATIONSHIPS AND BUSINESS RULES
· Model fundamental relationships
· Cardinality of relationships
o One-to-one
o One-to-many
o Many-to-many
· Is the relationship mandatory or optional?
· Naming the relationships

5. IDENTIFYING ATTRIBUTES
· Discover attributes for the subject area
· Assign attributes to the appropriate entity
· Name attributes using established naming conventions
· Documenting attributes

6. ADVANCED RELATIONSHIPS
· Modeling many-to-many relationships
· Model multiple relationships between the same two entities
· Model self-referencing relationships
· Model ternary relationships
· Identify redundant relationships

7. COMPLETING THE LOGICAL DATA MODEL
· Use supertypes and subtypes to manage complexity
· Use supertypes and subtypes to represent rules and constraints

8. DATA INTEGRITY THROUGH NORMALIZATION
· Normalize a logical data model
o First normal form
o Second normal form
o Third normal form
· Reasons for denormalization
· Transactional vs. business intelligence applications

9. VERIFICATION AND VALIDATION
· Verify the technical accuracy of a logical data model
· Use CASE tools to assist in verification
· Verify the logical data model using other models
o Data flow diagram
o CRUD matrix