Data modelling techniques, concepts and tools

What is data modelling?

In the world of Software Engineering, data modelling is a process which simplifies the data model of a software system. This data model is used as a basis, or blueprint, for creating a new and improved version of the system.

In data modelling, data is expressed as a series of symbols, diagrams and text to provide a visual representation of how the elements of data interrelate. 

Why is data modelling important?

When a database is created it isn’t done so in a vacuum where it is never to be improved, upgraded or evaluated. To organise data and make it available as required, databases need to be evolving. Data modelling enables improvement and consistency with security, naming conventions and rules, and ultimately supports the improvement of data analytics. 

Data modelling encourages better performing data with reduced errors, which means the overall quality of the data has improved.

Companies are also able to abide by national and global laws relevant to their industry regulations when data modelling is routinely applied.

If you want your teams to make data-driven decisions, then making data modelling a standard part of your organisation’s IT approach will ensure they are accessing the best quality data possible. 

Best data modelling techniques and concepts

There are five different varieties of techniques that are implemented to organise data, as follows:

• Hierarchical data model technique

As the name would suggest, this technique has a hierarchy to it, with a tree-like structure. Data is gathered to one root, which branches off containing other connected data, extending the tree.

You may see this technique applied in HR terms for a company structure, if it has a hierarchical approach, with a number of employees reporting to one department.

• Object-oriented data model technique

A collection of objects or components is referred to as an object-oriented data model. There are three types of models in this type of data design: Class, state and interaction.

• Relational data model technique

Data arranged into columns and rows within tables makes data easy to identify, as it is clearly ordered. This is one of the reasons it is a very popular model to implement. This technique is used to describe relationships between entities.

• Entity relationship model

Sometimes referred to as an ER model, this approach is taking real-life elements and the connections between them. This model groups data into general attributes, entity sets, relationship sets and constraints. 

• Logical data model

To devise a technical map of rules and structures for data, which can then be applied to specific project needs, a logical data model would be the answer. It’s a more honed understanding of data entities and the interconnectedness between them.

• Graph data model

Where the relational model focuses on information being arranged within a list format, this model spotlights the relationships occurring between information.

• Conceptual data model

Simple and abstract, the conceptual data model is very popular as it can communicate ideas with ease, which is important when presenting to a range of people, particularly if you are seeking buy-in with an idea. This model provides a structured business view of the data needed to ensure the business processes are optimal.