Data modelling services structure your data so analytics tools return fast, consistent, and trustworthy results. Without proper models, reports become slow, confusing, and unreliable, undermining confidence and increasing operational risk.

The four commonly recognised types of data models are:

1. Conceptual model – A high-level view of key business entities and how they relate to one another.

2. Logical model – A more detailed structure defining attributes, relationships and business rules.

3. Physical model – The implemented database design, optimised for storage and performance.

4. Semantic model – A business-facing layer that defines metrics, calculations and KPIs used in reporting tools.

Each type serves a different purpose, from strategy and design through to implementation and analytics.

Data modelling defines how an organisation’s data is structured, organised and connected.

It identifies the different types of data a business collects and generates, and clearly defines the relationships between them. By formalising these relationships, data modelling ensures consistent definitions, reliable reporting and scalable analytics.

In short, it turns scattered raw data into a structured foundation that supports accurate decision-making.

Yes, basic data modelling can be done in Excel.

Both Excel and Power Pivot allow you to create a data model by linking multiple tables through defined relationships. This enables more advanced analysis than flat spreadsheets alone.

However, Excel-based models are limited in scalability, governance and performance. For larger datasets and enterprise reporting, a dedicated data platform is typically required.

Data modelling is commonly performed using database and analytics platforms such as Microsoft Fabric, Azure SQL, Databricks, and enterprise modelling tools like ER/Studio or Power BI’s semantic modelling layer.

The right software depends on data volume, governance requirements and reporting needs.

Five common database models are:

1. Hierarchical

2. Network

3. Relational

4. Object-oriented

5. Document (NoSQL)

Each model defines how data is stored, structured and accessed within a database system.