5 August 2026

What is a data model?

A data model is a way to structure and organize a company’s data, as well as the relationships between them. The data model is the cornerstone of an information system, particularly MDM (Master Data Management) solutions deployed to manage a company’s strategic data.

Defining a Data Model

A data model defines and structures all of a company's data and the connections between them within the context of specific business processes. These data models are presented visually. They then help build an effective information system.

In fact, information systems require that data be properly defined, formatted, and organized beforehand. Data models help identify which data is required, structure it correctly, and build a common foundation for storing, accessing, sharing, updating, and utilizing data for all of a company’s teams, as well as for its partners, customers, and suppliers.

The Purpose of a Data Model

A data model is essential for building a reliable information system that supports all of the company’s business processes. The advantage of structuring data in a data model is that it supports a wide variety of use cases, including database modeling, information system design, and process development.

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Understanding the Different Data Models

There are three main types of data models. Companies typically use these three types of data models interchangeably. Companies generally benefit from all three data models, depending on the challenges they face.

The conceptual data model

Also known as a domain model, the conceptualdata model maps out the company’s major business processes. The conceptual data model clarifies the key initial concepts. It is most often used at the start of a new project and prior to the development of the logical data model.

The Data Model

The logical data model refines and elaborates on the conceptual data model. This logical model clarifies and describes the various logical entities (data classes), specifies the data attributes that define these entities, and characterizes the relationships between them.

The data logic model provides context for the conceptual data model. This allows data from different systems to be normalized.

The Physical Data Model

The physical data model is the final step in data modeling—or, to put it another way, the most complete and accurate model. In fact, the physical data model constitutes the internal schema of a database:

the tables
the columns in these tables
relationships between tables

The physical data model is used directly in database design. It generally allows for the design of three types of databases:

relational databases (traditional operational databases)
document databases (NoSQL and JSON)
Dimensional databases for aggregation and business intelligence data stores, such as data warehouses and data marts

MDM for Centralizing and Organizing Data

The data model, a prerequisite for MDM...

Building a data model will be the first step in structuring the data before implementing it in the MDM (Master Data Management) system.

Master Data Management will enable the management of all the company’s “master” data, just as a PIM (Product Information Management) system does specifically for product data and a DAM (Digital Asset Management) system does for graphic data.

Data models will be specific to each company. They may be very similar, or they may differ slightly or completely from one organization to another. For example, two companies in the same industry, such as the electricity sector, will have similar data models because they share the same characteristics and required data fields. On the other hand, a company in the IT sector and a company in the printing industry, for example—which face very different production and order management challenges—will have very different data models.

The data model must be designed early on so that a suitable MDM can subsequently be developed to properly manage a company’s data. The company can then distribute this data across its various communication channels, such as an e-commerce site or other platforms.

Master data

Reference data, also known as master data, constitutes a company’s strategic data —that is, the data on which its entire business is based. It is used throughout the company’s information system, particularly in line-of-business software.

The different types of reference data are as follows.

Product data: product description, model, color, size, retail price, unit of measure, diagram, instructions…
Customer data: name, mailing address, email address, order history, etc.
Supplier data: orders, shipments, inventory, invoices…
Geographic data or location data: information that enables the analysis of data by geographic area or by the locations of stores, branches, etc.
Digital assets: all digital files (images, videos, infographics, etc.) needed for the company’s communications
Legal information: standards, regulations, legal notices, etc.
Translations: texts in various languages…

The Role of Master Data Management

In many organizations, reference or strategic data is scattered across various applications. This is often referred to as data silos.

With an MDM, all data is consolidated into a single application and then distributed regularly to client applications as often as needed. The purpose of MDM (Master Data Management) is to ensure the integrity of the company’s data repository and to guarantee that all departments have access to accurate, relevant, and up-to-date data at all times. An MDM solution like Pimcore ensures data quality.

What are the key features of a Master Data Management solution?

MDM is a system for managing marketing data, product data (composition, ingredients, price, inventory, instructions, diagrams), customer data, employee data, supplier data, manufacturer data, and media data (images, videos, audio files, locations, etc.).

An MDM encompasses the functions of a PIM, a DAM, and a CMS.

Master Data Management tools therefore include several features:

Product Data Consolidation
Data Cleaning
Masking Sensitive Data
Enrichment, Classification, and Translation
Data Quality Control and Maintenance
Workflows and business processes (validation, collaborative completeness checks, etc.)
Supplier Management
Cross-Reference Management
Lifecycle Management
Multichannel Distribution
Bulk Deletion of Relationships

Why choose DATASOLUTION to deploy your centralized data repository?

DATASOLUTION advises and supports companies in their Martech digital transformation, enabling them to manage all their marketing data (products, customers, suppliers, location) and address the challenges of multichannel marketing by centralizing and organizing their data within a single data repository.

DATASOLUTION’s industry experts are available to help you define your requirements and propose a solution tailored to your specific needs.

Our team conducts an assessment of your IT marketing maturity to identify an architecture and an action plan to strengthen your IT, marketing, and sales ecosystem in line with your strategic priorities.