Key takeaways

• Data modelling is a strategic process that creates a visual representation of an information system, critical for simplifying, organising, and optimising databases, and supports business analysis and decision-making.
• There are various types of data models, including conceptual, logical, and physical models, each serving different purposes and providing different levels of abstraction, suitable for various audiences within an organisation.
• Data modelling techniques and processes are essential to accurately represent and organise data, with a range of available tools specifically designed to enhance the efficiency and effectiveness of database management and support evolving industry trends.

What is data modelling and why its important?

Data modelling is the process of creating a visual representation of an information system, illustrating the linkages among data points and organisational structures.

It serves as a blueprint for both the structure and the flow of data within an organisation. This visual representation aids in understanding and managing data, but its importance extends far beyond its graphical nature.

At its core, data modelling is about creating a system that not only stores information efficiently but also allows for effective retrieval and innovative use of that data. It helps organisations make sense of complex data landscapes, enabling them to harness the full potential of their information assets.

Furthermore, data modelling is not just a technical exercise; it is a strategic business activity. It supports business analysis and decision-making by providing a clear framework for data collection, storage, and use. It lays the groundwork for data-centric initiatives.

If you want to find out more about how you can use data in your business, take a look at this:

• Data Transformation: the complete guide for effective data management
• Creating a data-driven culture: a roadmap for organizational transformation
• The role of Business Data Analysis in a data-oriented project

What are the different types of data models?

Delving deeper into the labyrinth of data modelling, we encounter three distinct types of models:

• conceptual,
• logical,
• and physical models.

Each of these models serves different purposes and caters to diverse audiences within a company. From how data is physically stored in a database to the levels of data abstraction, these models pave the way for diverse approaches to data representation and organisation.

Conceptual data modelling

Conceptual data modelling, a type of conceptual model, is akin to painting a broad picture of a company’s data landscape. It sets up and defines business rules and concepts, providing an abstract representation of the conceptual data model necessary to support business requirements without being tied to any specific technology implementation.

This high-level approach focuses on how different data elements interconnect, and the overarching relationships that define the business domain. By doing so, it acts as a bridge between the technical data modelers and stakeholders, translating complex data structures into a language that everyone can understand.

Conceptual data modelling is the cornerstone of a robust data management strategy, paving the way for future growth and adaptation.

Logical data modelling

Logical data modelling is the process of creating a visual representation of the structure of an organisation’s data. Logical data models (LDMs) typically use diagrams to illustrate the relationships between different data entities, attributes, and the rules governing those relationships.

Logical data modelling is a vital step in bridging the gap between abstract concepts and tangible database structures. Its benefits, ranging from improved data quality to enhanced communication and better risk management, make it an indispensable tool for any organisation looking to leverage data effectively.

By investing in logical data modelling, businesses can ensure that their data systems are aligned with their goals, adaptable to change, and capable of supporting complex, data-driven processes.

Physical data modelling

A physical data model is the tangible manifestation of data organisation in the realm of data modelling. Often referred to as a physical model, it outlines the actual implementation of the database, including:

• data types
• sizes
• constraints
• relationships between tables

The design elements in physical data models, such as tables, columns, and relationships, directly influence the efficiency of business intelligence systems by effectively managing data elements.

Creating narrower tables helps to minimise long scans or reads, proving valuable for handling large data volumes or when operating across multiple tables. This is just one of the many ways physical data models contribute to optimal database performance.

Find out more about the data tasks and workflows:

• Data preprocessing: a comprehensive step-by-step guide
• Data classification: the backbone of effective data security
• Data visualisation: unlock insights in your data

Data modelling techniques

A toolbox for data modelling would be incomplete without an array of techniques to tackle diverse data scenarios. These tools include:

• Relational models
• Hierarchical models
• Network models
• Entity-relationship models
• Dimensional models
• Object-oriented models
• Graph models

Relational data modelling

Stepping into the realm of relational data modelling, we encounter a technique that has been ruling the roost since the mid-1990s. In this model, data is organised in a table-like structure where each table represents a specific entity and each row a specific record.

What sets relational data models apart is their ability to depict entities with various relationships, including:

• One-to-one
• One-to-many
• Many-to-one
• Many-to-many

The overarching purpose of the relational model is to describe different relationships between data entities, making it a popular choice in many database systems.

Hierarchical data modelling

Hierarchical data modelling arranges data in a tree structure consisting of one root and multiple connected data nodes. Within the model, relationships are represented as single one-to-many relationships between different levels of data.

A practical application of the hierarchical model can be seen in a supermarket where one parent department has several child aisles under it. Though originated in mainframe databases, hierarchical data models are still utilised by businesses today through systems like IMS.

Network data modelling

Unlike hierarchical database models, the network model allows for each record to have multiple parent and child records, thereby forming a more complex structure.

Adopted by the CODASYL Data Base Task Group in 1969, the network model was considered to offer a more natural way to model relationships between entities compared to the hierarchical model. Despite its heyday in the 1970s, the network model continues to influence modern database systems.

Entity relationship data modelling (E-R model)

The Entity-Relationship (ER) Model is a visual storyteller of data structures. It uses a diagrammatic approach to represent the structure of database systems, illustrating the relationships between different entities.

The ER Diagram is composed of entities, attributes, and relationships which are depicted using specialised symbols such as rectangles, ellipses, diamonds, and lines.

Introduced by Peter Chen in 1971, ER Diagrams provide a standardised modelling approach for conceptual database design. They include

• entities (represented by rectangles),
• attributes (classified into key, composite, multivalued, and derived attributes; represented by different shapes)
• and relationships (represented by diamonds).

Dimensional data modelling

Dimensional data modelling aims to simplify data structures for better performance and speed of data retrieval in a data warehouse environment. This modelling technique supports data reading, analysis, and summarisation processes.

While dimensional data models have numerous benefits, they also present certain limitations, such as the need for domain knowledge in designing schemas and challenges in maintaining data integrity during warehouse loading.

Object-oriented data modelling

Object-oriented data modelling (OODM) is a data modelling approach that uses object-oriented concepts to represent data structures and their relationships.

This methodology integrates the principles of object-oriented programming (OOP), where data is encapsulated within objects, and objects are instances of classes that define the attributes and behaviors (methods) they can have.

It provides numerous benefits, including natural mapping to real-world concepts, reusability, flexibility, and improved maintainability.

Graph data modelling

Graph data modelling is like a map of a city, depicting various landmarks (nodes) and the paths (relationships) connecting them. Nodes in a graph data model represent entities with a unique identity, and can contain properties that hold name-value pairs of data.

Relationships in a graph model connect nodes and are directional, representing actions or verbs between entities. Graph databases enforce the rule of no broken links, ensuring relationships always point to existing endpoints.

Properties in a graph model are attributes stored on nodes or relationships, which can answer specific queries about the data.

Data modelling: process and best practices

The data modelling process involves six steps:

1. Identifying business entities
2. Defining key properties
3. Creating a draft ER model
4. Identifying data attributes
5. Mapping attributes to entities
6. Finalising and validating the data model

This iterative nature of the data modelling process allows for continuous improvement and adaptation.

What else is worth noting – data modelers adhere to certain best practices to ensure the overall quality of the database. Data models should emphasise:

• Data completeness
• Accuracy
• Traceability
• Consistency

Applying data normalisation techniques can minimise redundancy and enhance flexibility in data models to support evolving business requirements.

Building data models around business processes facilitates easier navigation, evaluation of data, and appropriate placement within the model. To mitigate data bias and improve fairness in model predictions, techniques such as over-sampling, under-sampling, SMOTE, and population adjustment can be implemented.

Selecting the right data modelling tools

A variety of data modelling tools are available, offering broad compatibility and specialised features for SQL, NoSQL, and Cloud databases.

Here are the key steps to consider when selecting the right data modelling tools:

• identify your requirements (scope of modelling, complexity, scale and collaboration needs),
• evaluate key features (e.g. user interface, usability, integrations, customisation, reverse and forward engineering),
• assess performance and scalability,
• consider collaboration features and security,
• evaluate cost and licensing (pricing models and total cost of ownership),
• support and community,
• review case studies and testimonials.

By considering these factors, you can choose a tool that not only meets your current requirements but also scales with your organisation’s evolving needs.

And if you need a consultation or an IT partner for data solutions – do not hesitate to get in touch with us! Our specialists are prepared to support you in enhancing, controlling, and fully utilising your data resources!