Data Modelling

Data modeling (data modelling) is the process of creating a data model for the data to be stored in a database. This data model is a conceptual representation of Data objects, the associations between different data objects, and the rules. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.

Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data. Data Model structure helps to define the relational tables, primary and foreign keys and stored procedures. there are three types of data modelling which are discussed below (Conceptual, Logical and Physical). The main aim of conceptual model is to establish the entities, their attributes, and their relationships. Logical data model defines the structure of the data elements and set the relationships between them. A Physical Data Model describes the database specific implementation of the data model.

The main goal of a designing data model is to make certain that data objects offered by the functional team are represented accurately. The biggest drawback is that even smaller change made in structure require modification in the entire application.


Techniques of Data Modelling


There are three basic types of data modelling techniques :

  1. Entity Relationship Diagrams (ER Diagrams)

  2. UML Class Diagram

  3. Data Dictionary


1. Entity Relationship Diagrams

Also referred to as ER diagrams or ERDs. Entity-Relationship modeling is a default technique for modeling and the design of relational (traditional) databases. In this notation architect identifies:

  1. Entities representing objects (or tables in relational database),

  2. Attributes of entities including data type,

  3. Relationships between entities/objects (or foreign keys in a database).

ERDs work well if you want to design a relational (classic) database, Excel databases or CSV files. Basically, any kind of tabular data. They work well for visualization of database schemas and communication of top-level view of data.

ERD created with Dataedo.


Advantages:

  1. Easier to see the big picture

  2. Easier to understand table relations

  3. Possible to use visual cues to communicate information (e.g. location, proximity, color, shape)

Disadvantages:

  1. Doesn't work with large data models due to space constraints and clutter

  2. Supports limited amount of details