All You Need to Know About Data Modelling

In this digital era, the world is getting smaller and more connected every day. Every online interaction creates a data footprint which becomes an asset for businesses if used cleverly. As time goes and data accumulates, it gets increasingly difficult to make sense of it, and that's where data modelling comes in.

Data modelling is the process of expressing data and information in databases using text and symbols to make it easier to manage, analyze and update. It helps to have a solid grasp of data modelling techniques and their applications in order to develop an organized database that aids in decision-making and analysis. In this blog post, you will learn about the types of data models along with relevant examples from various industries. Additionally, you’ll discover common types of data modeling techniques and their usage in various scenarios. Let’s get started!

What is Data Modelling?

Data modelling is the process of organizing information in databases to make them easier to manage, analyse and update. The main goal of data modelling is to create a representation of your data that is accurate and easily understandable by the entire team. To put it simply, it’s the process of creating a picture of your data so that you can understand how it’s connected. There are many types of data models you can use to organise your data depending on the complexity of the system being modeled.

Data modelling diagrams are useful in managing databases, improving data accuracy and ensuring compliance with data security standards. Data modelling is a critical phase in the database or BI design process. In fact, the success of a project can depend on how well you have defined and documented the data model.

Types of Data Model

Data models are often visualized using diagrams that can be drawn manually or generated with the help of software. Business analysts and IT professionals use data modelling to build a comprehensive picture of the internal data landscape. Depending on the complexity of the system, one or more types of data models are used. Let’s take a look at the most common types of data modelling:

●Conceptual Data Model: It is a graphical depiction of database elements and their relationships, indicating the high-level user perspective of data. It concentrates on establishing entities, traits of an entity, and relationships between them rather than the specifics of the database itself.
●Physical Data Model: You can think of a physical data model as a logical data model with the extra details about the physical aspects of the data model, such as the data structure, tables and columns.
●Logical Data Model: The structure of the data entities and their interactions are further defined by this model. A logical data model is typically utilized for a particular project since its goal is to create a technical diagram of the rules and data structures.

Data Modelling Examples

The best way to understand a data model is to picture the building plan of an architect. An architectural building plan helps in putting up all subsequent conceptual models, and so does a data model. The following are some data modeling examples:

●ER (Entity-Relationship) Model: This model is based on the characteristics of real-world entities and the relationships among them. It creates an entity set, relationship set, general attributes, and constraints. In this case, an entity is a real-world object, take for example, an employee is an entity in an employee database. An attribute is a property with significance or value, and entity sets have attributes of similar value. Lastly, there's also the relationship between entities.
●Hierarchical Model: This particular model organises the data like a tree with a single root, to which other data is linked. The hierarchy starts with the root and branches out like a tree. It effectively explains several real-time relationships with a single one-to-many relationship between two kinds of data. For instance, a single supermarket can comprise several departments and many aisles. Thus, the ‘root’ node supermarket will have two ‘child’ nodes of (1) Pantry, (2) Packaged Food.
●Relational Model: This data model example puts the data into tables. The tables contain columns and rows, each cataloging an attribute present in the entity. It makes relationships between data points simple to identify. For instance, e-commerce websites can process purchases and track inventory using the relational model.
● Object-Oriented Database Model: This data model defines a database as an object collection, or recyclable software parts, with related methods and features. This type of data modeling is employed in engineering and architectural real-time systems used in 3D modeling.

Techniques in Data Modelling

There are three main data modelling techniques that can help you create an accurate data model. First, you have the Entity-Relationship Diagram or ERD technique used for modelling and designing of relational or traditional databases. Second, there is the UML or Unified Modeling Language class diagrams which is a standardized family of notations used for modeling and designing of information systems. Lastly, you have the Data Dictionary modeling technique where tabular definition or representation of data assets is performed.

Benefits of Data Modelling 

Below are some of the benefits of data modelling: 

●Minimizes software and database development errors
●Boosts enterprise-wide uniformity in system design and documentation
●Enhances database and application performance
●Simplifies data mapping across the entire organization
●Facilitates and hastens the conceptual, logical, and physical phases of database design
●It builds on business intelligence as it allows the identification of new opportunities by expanding data capability

Conclusion

Data modelling is more than just creating a data model diagram. It’s a process that involves understanding the data landscape and requirements of the business stakeholders. It’s important to remember that a data model diagram is only a representation of the data and not the data itself. When modelling data, you should always remember that your goal is to create a diagram that is understandable, accurate and easy to understand by the entire team. If a database is well-designed and fully documented, the staff members can use it effectively and efficiently. Data modelling is the critical phase in the data design process. In fact, the success of a project can depend on how well you have defined and documented the data model.