Information Requirement Analysis: Process modelling with physical logical data flow diagrams

1. Define System Scope: - Clearly outline the boundaries and scope of the system under consideration.
2. Collect Relevant Data: - Gather necessary information through stakeholder interviews and document analysis.
3. Context Diagram Creation: - Develop a high-level system representation, illustrating external entities and their interactions.
4. Logical Data Flow Diagram (DFD): - Conceptually map system processes, data stores, and flows without delving into implementation details.
- Identify primary functions and their interdependencies.
5. Data Store Identification: - Identify locations where data is stored and establish relationships between data stores.
6. Process Modeling: - Detail each process outlined in the logical DFD, outlining involved tasks and activities.
7. Refine Logical DFD: - Enhance the logical DFD based on feedback, ensuring accuracy and completeness.
8. Physical Data Flow Diagram (DFD): - Transition to a physical representation, specifying technology-specific details.
- Include hardware, software, data storage specifics, and network connections.
9. Data Store Refinement: - Elaborate on the physical aspects of data stores, such as database structures and storage locations.
10. Documentation Finalization: - Thoroughly document all elements of both physical and logical DFDs, providing detailed data flow descriptions and process narratives.
11. Review and Validation: - Engage stakeholders in a review process to ensure accuracy and completeness.
12. Ongoing Updates: - Regularly revise the diagrams based on feedback and any evolving requirements or insights.

This process aids in creating a comprehensive representation of information flow within a system, facilitating a deeper understanding of data processing and storage needs.

Data modeling with logical entity-relationship diagrams (ERD)

Data modeling with logical entity-relationship diagrams (ERD) is a process that involves visually representing a database's structure. Here's a breakdown:

1. Entities: - Entities represent real-world objects, concepts, or events pertinent to the database, such as customers, products, or orders.

2. Attributes: - Attributes are the properties or characteristics of entities. For example, a "Customer" entity might have attributes like "Name," "Address," and "Phone Number."

3. Relationships: - Relationships define the connections or associations between entities, illustrating how data should be organized.

4. Logical ERD: - The logical ERD focuses on business logic and relationship rules, abstracting away database management system details.

5. Normalization: - Normalization organizes data within a database to reduce redundancy and dependency, ensuring efficient storage and avoiding duplication. 6. Cardinality: - Cardinality defines numerical relationships between entities (e.g., one-to-one, one-to-many). It clarifies how instances of one entity relate to instances of another.

7. Diagram Representation: - A logical ERD is typically depicted graphically, with entities as rectangles, attributes as ovals, and relationships as lines. Cardinality notations indicate relationship nature.

8. Database Design Guide: - The logical ERD guides the design of the underlying database structure, aiding in table creation, field definition, and relationship establishment.

9. Tool Usage: - Various tools, physical and digital, can create logical ERDs, providing a visual interface for designing and manipulating entities, attributes, and relationships.

10. Documentation: - Proper documentation accompanies the logical ERD, explaining the purpose and rules of each entity, attribute, and relationship, facilitating understanding and maintenance of the database model.

In essence, data modeling with logical entity-relationship diagrams is pivotal for effective database design, offering a visual representation of entity relationships to ensure data organization and integrity.