Data Modeling

• Systems analysts
• Business analysts
• IT project managers
• Associate project managers
• Project managers
• Project coordinators
• Project analysts
• Project leaders
• Senior project manager
• Team leaders
• Product managers
• Program managers

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
  • Functional decomposition diagrams
  • Data flow diagrams
  • Use case models
  • Workflow models
  • Business rules
  • State diagrams
  • Class diagrams
  • Other documentation
• Types of modeling projects
  • Transactional business systems
  • Business intelligence and data warehousing systems
  • Integration and consolidation of existing systems
  • Maintenance of existing systems
  • Enterprise analysis
  • 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
  • One-to-one
  • One-to-many
  • 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
  • First normal form
  • Second normal form
  • 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
  • Data flow diagram
  • CRUD matrix

• GK2919, Business Analysis Essentials
• GK2964, Requirements Development, Documentation and Management
• GK2712, Use Case Modeling