Data Modeling

• Ways in which logical data models relate to requirements

• Identification of entities and attributes

• Determining relationships and business rules

• Data integrity using normalization

• Importance of logical data modeling when dealing with requirements

• When to use logical data models

• Relationships between logical and physical data models

• Logical Data Model Elements

• Reading a high-level data model

• Identifying Data Model Prerequisites

• Identifying information sources for data models

• Steps in developing a logical data model

2. Understanding The Context And Drivers Of A Project

• Importance of the content of a well-defined solution

• Uses of the functional breakdown diagram

• Uses of a Context-Level Data Flow Diagram

• Identifying requirements sources

  • Functional breakdown diagrams

  • Data Flow Diagrams

  • Using case templates

  • Workflow templates

  • Corporate Rules

  • Status diagrams

  • Class diagrams

  • Other documentation

• Types of modeling projects

  • Administrative systems for transactions

  • Business Intelligence and Data Storage Systems

  • Integration and consolidation of existing systems

  • Maintenance of existing systems

  • Business Analysis

  • Standard application

3. Conceptual Data Modeling

• Entity discovery

• Defining entities

• Documenting an entity

• Identifying attributes

• The distinction between entities and attributes

4. Conceptual Data Modeling – Identifying Relationships And Business Rules

• Modeling fundamental relationships

• Cardinality of Relationships

  • Bijective

  • One to many

  • Multivocal

• Mandatory or optional relationship?

• Designation of relationships

5. Attribute Identification

• Discover the attributes of the domain

• Assign attributes to the relevant entity

• Name attributes using established naming conventions

• Attribute Documentation

6. Superior Relationships

• Modeling multivocal relationships

• Modeling multiple relationships between the same two entities

• Modeling self-referential relationships

• Modeling ternary relationships

• Identifying redundant relationships

7. Development Of Logical Data Model

• Using supertypes and subtypes to manage complexity

• Using supertypes and subtypes to represent rules and constraints

8. Data Integrity Using Normalization

• Normalizing a logical data model

  • 1st normal form

  • 2nd normal form

  • 3rd normal form

  • Reasons for denormalization

• Contrast between transactional versus business intelligence applications

9. Verification And Validation

• Verifying the technical accuracy of a logical data model

• Use of CASE tools to assist verification

• Verifying the logical data model using other models

  • Data Flow Diagrams

  • CRUD Matrix