Data Analytics

Data modelling is a crucial step in the process of organizing and structuring data to represent the relationships and constraints within a database or information system. It provides a blueprint for designing databases that accurately reflect the real-world entities and their interactions.

Here’s a guide of the steps that we perform in data modelling for our customers:

1. Identify Business Requirements: Understand the business requirements and objectives that the database or system should address. Gather information about data sources, entities, relationships, and user needs.
2. Conceptual Data modelling: Create a high-level conceptual data model that represents the overall structure and relationships of the data without focusing on technical implementation. This model is often represented using Entity-Relationship (ER) diagrams.
3. Identify Entities and Attributes: Identify the key entities within the domain. For each entity, determine its attributes (properties) that hold relevant information.
4. Define Relationships: Define the relationships between entities. Specify whether the relationships are one-to-one, one-to-many, or many-to-many. Relationships add context and meaning to the data.
5. Normalize or De-Normalize Data: Apply normalization/ De-normalization techniques to ensure that the data is organized efficiently and without redundancy. Normalize the data to eliminate data anomalies and improve data integrity. De-Normalize the data to achieve faster data retrieval for Reporting needs.
6. Logical Data modelling: Translate the conceptual model into a logical data model that is more detailed and defines the structure of the database, including tables, columns, primary keys, and foreign keys. Use tools like Entity-Relationship diagrams or Unified modelling Language (UML) class diagrams.
7. Physical Data modelling: Convert the logical data model into a physical data model in the form of Tables and Views that's optimized for a specific database management system (DBMS).
8. Documentation: Document the entire data modelling process, including the conceptual, logical, and physical models, constraints, relationships, and any design decisions made.
9. Validation and Review: Review the data model with stakeholders, including domain experts, developers, and users. Validate that the model accurately represents the business requirements.
10. Iterate and Refine: Based on feedback and validation results, make necessary revisions to the data model. Data modelling is often an iterative process, refining the model until it accurately represents the domain.
11. Implementation and Integration: Work closely with database developers to implement the data model in the chosen DBMS. Ensure that the physical implementation matches the logical design.
12. Testing and Validation: Test the implemented database for functionality, data integrity, and performance. Validate that the database meets the defined business requirements.
13. Maintenance and Evolution: As the business evolves, we adapt and modify the data model. Regularly review and update the data model to accommodate changes in the business environment.