What is a data model? How do I create one?

Data model definition

A data model is a structured representation of a set of data and objects, and the relationships between them, within an application, table or system. The data model defines how data is organized, centralized and managed within an IS system or larger IT environment. It defines the relationships between the various objects in the table.

The data model can be used to define a number of characteristics specific to the data, such as its attributes or links. There are 3 types of data model:  

• The conceptual data model, which characterizes business concepts and the relationships between them, without taking into account implementation details.

• Logical data model. It represents the logical way in which data is stored and ordered within a particular system. It takes into account data types, foreign keys and primary keys.

• The physical data model. It focuses on how data is stored at the lowest level, often using representations such as tables, columns, etc.

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Purpose of the data model

Why create a data model? Data models enable companies to guarantee the unquestionable reliability of their information. Creating a data model that is consistent with your industry and your activity also means ensuring that your data is used serenely and efficiently.

Thus, designing a data model is a process that provides companies with qualitative, comprehensive and reliable data. Data modeling reduces the risk of errors in the use of this information, and gives you confidence in your data.

The creation of a data model serves several purposes: 

• Data organization. Creating a data model means ensuring that data is organized in such a way that it is structured, easily accessible and comprehensible to everyone. This facilitates data management and use within an organization.

• Data understanding. 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 data and its relationships. All this also contributes to a better understanding of employees' functions and processes.

• Standardization. Data models often incorporate normalization 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. In a data model, there are a number of rules and constraints that promote data reliability and integrity, so that data remains consistent and accurate.

• Facilitating software development. A data model provides a solid foundation for software and platform development, as it defines the structure of the data with which applications and other systems will interact. This greatly facilitates the creation of well-integrated, coherent systems and platforms.

• Interoperability. By creating a standardized presentation of data, the data model helps to make interoperability easier between different systems and software. This can be particularly valuable when a company operates in an IT environment where several systems need to exchange data.

• Performance optimization. Data models can play a powerful role in the performance of organizations by optimizing databases and judiciously defining the structure of indexes, tables and so on.

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How do you create a powerful, relevant data model?

The creation of a data model consistent with the company's activity and objectives will be defined in several stages, from the identification of needs to the definition of data organization.

1. Identify business needs. This stage consists in understanding the requirements and objectives of each of the contributing business lines and data users for whom the company is creating the data model. We then need to collaborate effectively with the various stakeholders to understand the business processes and key information required.

2. Define business entities. Be sure to identify the main business entities in the business domain(s) you're modeling, as these are the main elements you'll be storing within your database.

3. Identify the attributes of each entity. At this stage, you need to identify the attributes that describe the specific characteristics of each entity.

4. Establish relationships between entities. Establish links and relationships between each entity.

5. Standardize the data model. Here, the principles and rules of normalization mentioned above must be applied to eliminate redundancies and reduce the risk of data errors.

6. Establish reliability constraints. In this step, you define referential integrity constraints to guarantee data consistency and reliability between different tables, columns, etc.

7. Use data modeling tools like Excel to visually create your ideal data model.

8. Document the data model. Establish clear, precise and detailed documentation of your data model, incorporating the rules, constraints, processes and logic of data model creation.

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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 IS system. While the data model meets the objectives of data organization and structuring, the PIM collects, centralizes, manages and distributes all an organization's product data.

A PIM solution therefore uses the underlying data model to store and manage all data related to product information. The data model is used to define the relationships between products and how they are linked to each other, by their attributes, categories and so on. In this way, the data model is used by PIM , and the two entities work together to efficiently manage all data linked to an organization's products.

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A PIM platform is the ideal tool for managing your data model and keeping it consistent and relevant to your business needs and objectives. PIM enables you to configure the entities, attributes and relationships between your product data in accordance with the data model established upstream. 

On the PIM interface, you can also manage the relationships or "links" between your various products and parent categories, for example, while ensuring that the relationships conform to the data model. A PIM like Quable's offers you the possibility of creating and applying validation rules, thanks to workflows for example, according to your data model to always maintain a relevant, qualitative and consistent data model.

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The Quable data model on the solution PIM

PIM Quable offers a number of features to help you manage your data model effectively. Attributes and attribute sets, for example, enable users to configure the structure and organization of your product information, as well as define information groupings for different document types.

Link types also enable you to characterize the links between your documents, so that you can link them at a later date. Managing your data model on PIM Quable simplifies the administration of your product content. PIM Quable provides fast, fluid navigation of your hierarchy and objects. Finally, you can localize the properties of your attributes in all the interface languages of your PIM platform.

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