Nowadays, data modelling is a key part of developing a data governance framework in every organisation. This involves structuring and designing the data in a comprehensive and consistent way.

It provides the company with an opportunity to understand its business needs and translate them into data structures. It enables entities and their attributes to be identified and the relationships between them to be clearly defined.

Doing this establishes a solid foundation for clear and effective data governance and access management.

This article will introduce you to the key concepts of data modelling, including how to model data, and explain why data modelling is important when deploying your data governance strategy.

The Three Levels of Data Modelling: Conceptual, Logical, and Physical

When modelling data, organisations have several options to consider, as there are three main ways of representing data in order to translate business needs.

Firstly, the conceptual view provides a representation of entities and their relationships, independent of the technical specifications of the underlying database. This abstraction enables effective communication between business stakeholders and technical teams, ensuring that the data model accurately reflects organisational requirements.

Then, the logical view moves closer to designing the actual database structure, representing entities as tables and defining their attributes. At this stage, each table usually contains a primary key that identifies each record uniquely and prevents duplicate entries, thereby ensuring data integrity. It also establishes relationships between tables using foreign keys, laying the groundwork for the physical implementation.

Finally, the physical model specifies how the data model is implemented within a particular database management system. Using Structured Query Language (SQL) for example, it translates the logical design into actual database objects, including the creation of tables, their indexation, and relationships. At this stage, the focus is on optimising performance, storage and security within the chosen environment.

Key Concepts in Data Modelling: Entities, Attributes, Relationships, and Cardinality

There are some key concepts in the data modelling process that facilitate collaboration among global business teams and ensure a shared understanding. The process starts with an entity, which represents a real-world object or concept that can be identified within the database while it is stored. A car, for example, is an entity that can be represented in the modelling process.

Additionally, entities have attributes that describe characteristics, such as the colour or brand of a car, or specific properties, such as the owner's name or phone number.

Once you have designed the entities and their attributes, you can connect them by defining the relationships that describe how the tables interact with each other. For instance, in a car-related data model, a relationship could be created between a 'Car' table and a 'Person' table. Each car record could include a reference, such as a foreign key, to identify its owner.

Finally, cardinality defines the number of possible relationships between entities — in this case, between 'Person' and 'Car'. A one-to-many (1:N) relationship, for example, means that one person can own several cars, but each car belongs to only one person. Conversely, a many-to-many (N:N) relationship could represent a situation in which multiple individuals share ownership of several vehicles, as in a car-sharing service.

Data Modelling for Quality, Compliance, and Stronger Data Governance

As mentioned above, data modelling is a crucial process for your organisation. Different types of data model help companies establish common foundations that can be easily understood by technical and business teams alike. In other words, data modelling enables employees to collaborate more effectively by providing them with a shared language that overcomes technical barriers.

Beyond this, it improves data quality by designing data structures and relationships, preventing inconsistent data, errors and redundancies, and enhancing compliance thanks to clear definitions of data that support the meeting of regulatory requirements and internal policies.

Also, accurate and well-structured data forms the basis of effective decision-making. When data is organised and reliable, organisations can analyse trends with confidence, uncover opportunities and make informed, data-driven decisions.

Finally, a robust data model allows for future growth by providing the flexibility to integrate new data sources, adopt emerging technologies and efficiently scale operations while maintaining data consistency and integrity.