A Comprehensive Guide to Data Modelling Types, Approaches, and Use Cases

Introduction

Data Modelling is a cornerstone in database design, software development, and analytics. It provides a structured approach to defining how your data is stored, organised, and accessed, in order to ensure consistency, efficiency, and scalability across different applications. Choosing the right data model is imperative, as different models serve distinct business needs and technical requirements. Some models are optimised for transactional systems, while others are designed for analytics, scalability, or complex relationships.

Fortunately, Data Modelling follows a structured approach that typically includes three key stages: conceptual, logical, and physical modelling, each of which play a crucial role in transforming high-level business requirements into fully optimised database systems.

In this guide, we’ll explore these approaches, various types of data models, their comparative advantages, and real-world use cases for when to use each type of model.

Data Modelling Stages

Data Modelling can typically be broken down into three stages, each of which provides a different level of support to the project as a whole:

Conceptual Modelling

Conceptual models provide a high-level representation of how data is structured without delving into any amount of technical detail. They define entities, attributes, and relationships, often using Entity-Relationship (ER) diagrams, which are a visual representation of a database’s structure and illustrate how various entities connect with one another – more on that later! These conceptual models are used to communicate data structures with non-technical stakeholders and serve as a blueprint for more detailed models.

Logical Modelling

Logical models introduce a structured, database-agnostic approach, defining specific attributes, relationships, and normalisation rules. This ensures data integrity and minimises duplication (AKA redundancy). Logical models focus on defining primary and foreign keys, constraints, and indexing strategies while remaining independent of any specific database technology.

Physical Modelling

Physical models represent the actual implementation of data storage within a database system. They define table structures, indexes, partitions, and performance optimisation techniques specific to a chosen database engine. Physical Modelling ensures that the data structure aligns with the system’s performance and scalability requirements.

Types of Data Models

Entity-Relationship (ER) Model

The entity-relationship model represents data graphically using entities, attributes, and relationships, and is primarily used in database design before implementation, helping define the structure and relationships of a database. ER models form the foundation for relational databases by providing a blueprint for table structures and relationships.

Relational Model

The relational model organises data into tables consisting of rows and columns, with relationships maintained through primary and foreign keys. It ensures data integrity through normalisation, reducing redundancy and improving consistency. This model is widely used in transactional (i.e. OLTP) systems where data accuracy and structured querying are of utmost importance. Examples of relational databases include MySQL, PostgreSQL, and SQL Server.

Hierarchical Model

The hierarchical model structures data in a tree-like format, where each record has a single parent but can have multiple children. This model is efficient for specific query types but lacks flexibility, making it difficult to restructure data relationships. Hierarchical systems are generally used in legacy systems such as IBM IMS and for XML data storage, but have mostly been replaced by more flexible and scalable models (e.g. relational and NoSQL databases, depending on use case). There are specific use cases in which their structure provides advantages, such as Microsoft Active Directory and LDAP, but these are few and far between.

Network Model

The network model extends upon the foundations of the hierarchical approach by allowing many-to-many relationships between records. This makes it more flexible and capable of representing complex relationships. The network model was widely used in early database systems, such as CODASYL databases, but has been largely replaced by relational and NoSQL models. Again, however, there are a few specific modern use cases, such as high-performance transactional systems (e.g. airline reservations and supply chain management).

Object-Oriented Model

The object-oriented model stores data as objects, not dissimilar to how data is represented in object-oriented programming languages. This model is particularly useful for applications requiring complex data structures and behaviours, such as multimedia databases and CAD systems. Examples of object-oriented databases include db4o and ObjectDB.

Dimensional Model

The dimensional model is specifically designed for data warehousing and analytics. It structures data into fact tables, which store transactional or event data, and dimension tables, which contain descriptive attributes. This model is optimised for fast querying and reporting. Two common schema types within the dimensional model are the star schema, which simplifies relationships between fact and dimension tables, and the snowflake schema, which normalises dimension tables to reduce redundancy at the cost of increased complexity. Examples of databases that support dimensional Modelling include Amazon Redshift, Google BigQuery, and Snowflake.

NoSQL Model

The NoSQL model is designed for scalability, high performance, and flexible schemas, making it ideal for handling unstructured or semi-structured data. It consists of several subtypes, each optimised for different use cases. Key-value stores, such as Redis and DynamoDB, store data in simple key-value pairs for fast lookups. Column-family stores, like Cassandra and HBase, are optimised for large-scale analytics. Document stores, including MongoDB and CouchDB, use JSON-like documents to handle semi-structured data. Graph databases, such as Neo4j and ArangoDB, excel in scenarios that require efficient relationship management.

Breakdown of Data Model Types:

Conclusion & Future Trends

Data Modelling is a critical aspect of database and system design, and impacts everything from performance to scalability and maintainability. Choosing the right model depends on your specific use case, and whether you need the structured integrity of relational databases, the flexibility of NoSQL, or the analytical power of dimensional models.

As data needs continue to evolve, we’re seeing increased adoption of hybrid models that combine the best aspects of multiple approaches. AI-driven data Modelling, real-time analytics, and automated schema generation are also shaping the future of database management. Ensuring you keep yourself informed about these trends will, in turn, help ensure your data infrastructure remains agile and competitive.

Need help selecting the right data model for your business? At Vertex Agility, we specialise in optimising data strategies, ensuring your systems are scalable, efficient, and future-ready.

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