Data Modelling

Data Modelling

Concepts of Data Modelling

• Data modelling refers to the practice of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the data flow and structure.
• A data model helps in understanding the nature, structure, and flow of information that is to be managed in a task or project.
• Data models are used to define and analyse data requirements needed to support the business processes.

Types of Data Models

• Conceptual Data Models: High-level, static business structures and concepts.
• Logical Data Models: Detailed data models of business requirement without any reference to the physical database’s structure.
• Physical Data Models: Representing how the model will be built in the database, detailed with all the tables, columns, and relationships.

Components of Data Models

• Entities, which are the main objects or subjects being discussed in the system.
• Attributes, which describe the properties of an entity.
• Relationships, which define how entities are connected to each other.

Benefits of Data Modelling

• Data modelling makes it easier to understand complex database concepts.
• It aids in visualising the rules of business and how they interact with the database.
• Ensures that all requirements are met during the creation of the database.
• It can be a good documentation tool for reference purposes.
• Performance is improved as the model is efficient and well-structured.

Data Modelling Techniques

• The use of Entity-Relationship diagrams (ERD) to represent the structural data of a business domain in the form of entities, attributes and relationships.
• Use of UML Diagrams that use a set of graphic notation techniques to create visual models of object-oriented software-intensive systems.
• Data dictionary, a centralized repository of information about data such as its meaning, relationships to other data, origin, usage, and format.