Data modelling and its techniques emerged in the 1960s as databases became more widely used on mainframes and then minicomputers. It enabled organizations to bring consistency, repeatability and disciplined development to data processing and management. That’s still the case, but the techniques used to create data models have evolved along with the development of new types of databases and computer systems.

These are the data modeling approaches used most widely over the years, including several that have largely been supplanted by newer techniques.

What are the Data Modelling Techniques?

Below given are 5 different types of techniques used to model the data:

1. Object-Oriented Model
2. Entity-Relationship Model
3. Network/Dimensional Model
4. Relational Model
5. Hierarchical Model

Object-Oriented Model

In the 1990s, object-oriented data modeling emerged. 

The object-oriented approach is similar to the ER method in how it represents data, attributes and relationships, but it abstracts entities into objects. Different objects that have the same attributes and behaviors can be grouped into classes, and new classes can inherit the attributes and behaviors of existing ones. But object databases remain a niche technology for particular applications, which has limited the use of object-oriented modeling.

Entity-Relationship Model

These models map entities, their attributes and the relationships between different entities. Use formal diagrams to represent the relationships between entities in a database. Several ER modeling tools are used by data architects to create visual maps that convey database design objectives.

For example, the attributes of an employee data entity could include last name, first name, years employed and other relevant data. ER models provide an efficient approach for data capture and update processes, making them particularly suitable for transaction processing applications.

Dimensional Model

Dimensional data models are primarily used in data warehouses and data marts that support business intelligence applications. While relational and ER models emphasize efficient storage, dimensional models increase redundancy in order to make it easier to locate information for reporting and retrieval.

 They consist of fact tables that contain data about transactions or other events and dimension tables that list attributes of the entities in the fact tables. 

For example, a fact table could detail product purchases by customers, while connected dimension tables hold data about the products and customers. Notable types of dimensional models are star schemas, which connect a fact table to different dimension tables, and snowflake schemas, which include multiple levels of dimension tables.

Relational Model

The relational data model was created as a more flexible alternative to hierarchical and network ones. The relational model maps the relationships between data elements stored in different tables that contain sets of rows and columns. 

Relational data modeling doesn’t require a detailed understanding of the physical properties of the data storage being used. In it, data segments are explicitly joined through the use of tables, reducing database complexity.

Hierarchical Model

Hierarchical data models organize data in a treelike arrangement of parent and child records. A child record can have only one parent, making this a one-to-many modeling method. The hierarchical approach originated in mainframe databases — IBM’s Information Management System (IMS) is the best-known example. 

Though this approach is less efficient than more recently developed database models, it’s still used in Extensible Markup Language (XML) systems and geographic information systems (GISs).