Data Modeling Explained

What is Data Modelling?

Data modelling is the visual representation of how information is organised, linked and used within an organisation. It is based on the creation of models that illustrate entities (customers, products, transactions, employees) and the relationships between them.

This representation enables teams to understand the logic behind the data and to ensure that the structure is clear, consistent and easy to use. For example, a well-designed model shows how sales are linked to products, how these products are associated with suppliers, or how payments are linked to customers.

The Different Types of Data Model

Data modelling takes place at several levels of representation, ranging from a global, abstract view to a detailed technical structure. Each type of model plays a role in the understanding, design and implementation of enterprise databases.

The conceptual model

The conceptual model represents an overview of the data without worrying about the technical aspects. It enables business needs to be understood and the main entities (customers, products, employees, projects, invoices) and their logical relationships to be identified.

This model answers the question: "What data do we need to support our business processes?"

The conceptual model thus helps to frame the project and align the IT and business teams before entering the technical phase.

The logic model

The logical model translates the conceptual model into a more detailed and standardised structure, while remaining independent of the technology used.

It defines :

• Tables (or entities),

• Fields (or attributes),

• Relationships (1-to-1, 1-to-many, many-to-many),

• And associated business rules.

It is used to plan the data structure before implementing it in a real database.

The physical model

The physical model is the concrete translation of the logical model into a database management system (DBMS) such as SQL Server, Azure SQL, or Dataverse for Power Platform.

It includes all the technical details needed to create the tables:

• Data types (text, integer, date),

• Index, primary and foreign keys,

• Integrity constraints,

• Performance and storage.

This model is used by developers and administrators to build databases and ensure their performance and security.

What are the benefits of data modelling?

Improving data quality and reliability

Well-structured modelling helps to reduce errors, eliminate duplication and ensure consistency between different systems. Each piece of data is clearly defined, documented and linked to its origin, facilitating traceability and regulatory compliance (such as Bill 25 in Quebec or RGPD in Europe).

Optimising performance and operational efficiency

Thanks to a clear, standardised architecture, information systems can process and interrogate data more quickly. This translates into better performance of Power BI reports, reduced loading times, and more efficient use of IT resources.

Facilitating strategic decision-making

Modelling provides a unified, reliable view of all the company's data. This gives decision-makers accurate, consolidated information with which to analyse performance, identify trends and anticipate needs.

This is the basis for high-performance business intelligence, connected to tools like Power BI or Azure Synapse Analytics.

Strengthening governance and compliance

Good modelling is part of a data governance approach. It defines who owns, who uses and how each piece of data should be managed. This avoids information silos, strengthens security and ensures compliance with legal and standards requirements.

How do you successfully model your data?

The success of a data modelling project depends on a structured methodology combining interdisciplinary collaboration, technical rigour and solid governance. Here are the steps you need to take to build a model that is effective, sustainable and aligned with your business objectives.

Step 1 - Identify business needs and data sources

The first step is to define the objectives of the model and understand the real needs of the end users: management, finance, human resources, operations or marketing.

This phase must be carried out in close collaboration between IT and business teams, to avoid creating models that are disconnected from operational reality.

Examples:

• An HR team wants to consolidate payroll, absence and performance data.

• The finance department wants to consolidate flows from several accounting systems.

The data sources also need to be inventoried: internal databases, Excel files, CRM, ERP, cloud tools or external databases (API, supplier portals, etc.).

Stage 2 - Designing the conceptual and logical models

Once the requirements have been defined, the conceptual and logical models are developed to structure the relationships between the entities.

These diagrams form the basis for building the physical model and facilitate communication between the technical and business teams.

Recommended tools:

• UML diagrams or MERISE to represent entities and their relationships.

• Power BI and Microsoft Fabric to visualize relationships between datasets.

• SQL Server Management Studio (SSMS) to test table structures and relationships.

Stage 3 - Defining governance and quality rules

Data governance is the cornerstone of a sustainable business model. It aims to guarantee the reliability, security and compliance of the information stored and shared.

Points to check:

• Detection and deletion of duplicates.

• Standardization of formats (dates, amounts, units).

• Securing access according to user roles.

• Compliance with privacy laws (Law 25, RGPD).

Stage 4 - Implementing and testing the model

Once the design has been finalised, comes the technical implementation stage in the chosen environment. This phase validates the model's performance, consistency and compatibility with the analysis and reporting tools.

Objectives of this phase :

• Check the speed of query execution.

• Confirm the consistency of consolidated data.

• Ensure the quality of visualisations and key indicators (KPIs).

Structure your data, speed up your decisions

Data modelling is the foundation of any successful digital strategy. It enables volumes of raw information to be transformed into a clear, coherent and usable vision, supporting decision-making and growth.

With this in mind, our digital transformation consultants support organisations in designing robust, secure and scalable data models. Contact our experts today for a customised solution.