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Data modelling

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An important part of the bigger picture

Data modelling in the context of composable commerce

In the world of composable commerce, where flexibility and adaptability are central, data modelling plays a crucial role. A well-designed data model provides the foundation for seamless communication and collaboration between the various components (such as PIM, DAM, CMS and OMS). Here, we explain how data modelling works in this context and what an effective model for composable commerce could look like.

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The foundations

Core principles of data modelling in composable commerce

Central data structure

Since composable commerce relies on integrating different specialised systems, it is important to create a central data structure that acts as a "single source of truth". This structure ensures that all components have access to consistent, up-to-date data.

Flexibility and extensibility

The data model must be flexible enough to accommodate changes and extensions quickly, whether that means introducing new products, adding new sales channels or integrating new technologies.

Decoupling components

Decoupling individual systems (e.g. frontend from backend, PIM from DAM) allows the data model to be designed so that each system operates independently without compromising the integrity of the overall architecture.

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THE ESSENTIAL BUILDING BLOCKS

Key components of the data model

Product information (PIM)

  • “Product” entity: Includes core data such as SKU, name, description, price and attribute values. This entity is closely linked to the PIM system and serves as the central source for all product-related data.
  • Links to categories: Products are organised in a hierarchical category structure, making them easy to navigate and manage.
  • Relationships with digital assets: Each product can be linked to multiple digital assets (e.g. images and videos) managed in the DAM system.

Digital assets (DAM)

  • “Digital Asset” entity: Includes information such as file path, media type, metadata and usage rights. This entity is linked to the DAM system and used in various contexts, such as product presentation or marketing campaigns.
  • Relationships with products and categories: Digital assets are linked not only to products, but also to categories or brands, allowing them to be used in a variety of ways

Orders and transactions (OMS)

  • “Order” entity: Stores all relevant order information, including customer data, products ordered, and payment and shipping details. This entity is linked to the Order Management System (OMS).
  • Relationships with products and customers: An order contains references to the products purchased and the customer who placed the order, ensuring full traceability.

Customer information (CRM)

  • “Customer” entity: Contains all customer information, such as contact details, purchase history and preferences. This data is closely linked to the Customer Relationship Management (CRM) system.
  • Personalisation and segmentation: The data model should support customer segmentation and personalised offers based on customer behaviour and preferences.

Content and presentation (CMS and frontend)

  • “Content” entity: Stores all text and visual content used in the online shop, such as blog posts, product descriptions and landing pages. This entity is linked to the Content Management System (CMS).
  • Relationships with products and categories: Content can be linked to specific products or categories to support targeted marketing campaigns or SEO activities.

A proposed data model for composable commerce

Modular approach

Create separate data models for each core component (PIM, DAM, OMS, CRM, CMS), connected through standardised APIs. This modular approach allows each component to be updated or replaced independently, without having to remodel the entire system.

API-first strategy

Data modelling should be designed from the outset to make all data accessible through APIs. This simplifies the integration of new systems and data exchange between existing components.

Central data repository

Consider implementing a central data repository that acts as a "single source of truth" and ensures that all systems have access to consistent, up-to-date data. This could be achieved, for example, through a cloud-based solution that is updated in real time.

Data hierarchies and relationships

Use hierarchies and relationships between entities to make data easier to navigate and manage. Linking products to categories and digital assets is a good example of this.

Data validation and governance

Implement data validation mechanisms to ensure that only high-quality, consistent data enters the system. A governance framework should also be in place to ensure data integrity and security.

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