COURSE OBJECTIVE:
• How logical data models relate to requirements
• Identifying entities and attributes
• Determining relationships and business rules
• Data integrity through normalization
TARGET AUDIENCE:
• 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
COURSE PREREQUISITES:
• GK2919, Business Analysis Essentials
• GK2964, Requirements Development, Documentation and Management
• GK2712, Use Case Modeling
COURSE CONTENT:
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
FOLLOW ON COURSES:
Not available. Please contact.