What is a data model? How do you create one?
22 November 2023
Definition of a data model
A data model is a structured representation of a set of data and objects, as well as the relationships between them, within an application, a table or a system. The data model defines how data is organised, centralised and managed within an IT system or a wider IT environment. It defines the relationships between the various objects in the table.
The data model can be used to define various characteristics specific to the data, such as its attributes or links. There are three types of data model:
- The conceptual data model, which describes business concepts and the relationships between them, without taking implementation details into account.
- The logical data model. This represents the logical way in which data is stored and organised within a particular system. It takes into account data types, foreign keys and primary keys.
- The physical data model. This focuses on how data is stored at the lowest level, often using representations such as tables, columns, etc.
Purpose of the data model
Why create a data model? Data models enable the organisations that design them to guarantee the absolute reliability of their information. Creating a data model that is consistent with one’s industry and business also ensures that data can be used confidently and efficiently.
Thus, designing a data model is akin to a process that enables businesses to access high-quality, comprehensive and reliable data. Data modelling reduces the risk of errors when using this information and ultimately gives you confidence in your data.
Creating a data model therefore serves several purposes:
- Organising data. Creating a data model ensures that data is organised in such a way that it is structured, easily accessible and understandable to everyone. Consequently, this facilitates the management and use of data within an organisation.
- Understanding the data. An effectively structured data model provides a visual and structural understanding of the data. This greatly helps developers, analysts and users to understand the meaning of the data and the relationships between it. All of this also contributes to a better understanding of employees’ roles and their processes.
- Normalisation. Data models often incorporate normalisation processes into their design. These processes aim to reduce anomalies and the risk of errors in the database, leading to a more efficient data structure.
- Data reliability. A data model contains several rules and constraints that promote data reliability and integrity, ensuring that the data remains more consistent and accurate.
- Facilitating software development. A data model provides a solid foundation for the development of software and IT platforms, as it defines the data structure with which applications and other systems will interact. All of this greatly facilitates the creation of well-integrated and consistent systems and platforms.
- Interoperability. By creating a standardised representation of data, the data model helps to facilitate interoperability between different systems and software. This can be particularly valuable when a business operates in an IT environment where multiple systems need to exchange data with one another.
- Performance optimisation. Data models can play a powerful role in an organisation’s performance by optimising databases and carefully defining the structure of indexes, tables, etc.
How do you create a powerful and relevant data model?
Creating a data model that is consistent with the organisation’s business activities and objectives involves several stages, ranging from identifying requirements to defining the organisation of the data.
- Identifying business requirements. This stage involves understanding the requirements and objectives of each of the business units that contribute to and use the data for which the company is creating the data model. It is therefore necessary to collaborate effectively with the various stakeholders to understand the business processes and the key information required.
- Defining business entities. Ensure you identify the main business entities within the business domain(s) you are modelling, as these are the primary elements you will store within your database.
- Identify the attributes of each entity. At this stage, you must identify, for each entity, the attributes that describe its specific characteristics.
- Establish relationships between entities. Define the links and relationships between each entity.
- Normalise the data model. Here, you must apply the normalisation principles and rules mentioned previously in order to eliminate redundancies and reduce the risk of errors in the data.
- Establish integrity constraints. In this step, you must define referential integrity constraints to ensure data consistency and reliability across different tables, columns, etc.
- Use data modelling tools such as Excel to visually create your ideal data model.
- Document the data model. Produce clear, accurate and detailed documentation of your data model, incorporating the rules, constraints, processes and logic involved in its creation.
How to manage the data model: the role of the PIM
The PIM (Product Information Manager) is closely linked to the data model within a company’s IT system. Whilst the data model fulfils the objectives of organising and structuring data, the PIM collects, centralises, manages and distributes all of an organisation’s product data.
A PIM solution therefore uses the underlying data model to store and manage all data relating to product information. The data model enables the definition of relationships between products and how they are linked to one another, through their attributes, categories, etc. Thus, the data model serves the PIM, and the two entities work in tandem to effectively manage all data relating to a company’s products.
A PIM platform is the ideal tool for managing your data model and keeping it consistent and relevant in line with your business and organisational needs, as well as your objectives. PIM allows you to configure the entities, attributes and relationships between your product data in accordance with the data model established beforehand.
On the PIM interface, you can also manage the relationships—also known as ‘links’—between your various products and their parent categories, for example, whilst ensuring that these relationships comply with the data model. A PIM such as Quable’s enables you to create and apply validation rules – through workflows, for example – based on your data model, to ensure that the data model remains relevant, high-quality and consistent at all times.
The Quable data model in the PIM solution
With regard to the Quable PIM, several features are available to manage your data model effectively. Attributes and attribute sets, for example, allow users to configure the structure and organisation of your product information, as well as to define how information is grouped for different types of documents.
Link types also enable you to define the relationships between your documents so that they can be linked at a later stage. Consequently, managing your data model within the Quable PIM simplifies the administration of your product content. The Quable PIM offers smooth and rapid navigation when designing your hierarchy and objects. Finally, you can localise your attribute properties into all the interface languages supported by your PIM platform.