Understanding the data model, from concept to activation

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Nearly 6 out of 10 business leaders today believe that data is a vital lever for growing their company (Jedha). A figure that mirrors the strong growth in the data warehouse and data lake markets.

In most organisations, data is scattered across multiple systems. To become usable and valuable, it needs to be reliable, consistent, and structured according to a well-defined schema.

🔎 What exactly is a data model? What types exist, and what role does it play in data management? What are its benefits and applications, from operational steering to marketing use cases? 💡

Its role is simple but essential: to organise, standardise, and ensure consistency across data. Each object is clearly defined, with a primary key serving as a unique identifier.

Depending on the situation, the data model may precede the database to define its architecture, or it may be created afterwards to document or optimise an existing database.

It makes data easier to use, whether in software, a marketing platform, or BI analysis.

Building a data model isn’t just a technical task, it’s the foundation of any data-driven strategy.

The semantic layer unifies how data is understood and used.

💡 Imagine a retailer without a clear data model: product data is stored in various formats, and some orders are missing key fields.

The resulting reports show discrepancies, the analyses are skewed, and teams can’t rely on the figures. Strategic decisions are then built on a shaky foundation.

A data model typically includes three levels, which follow a logical sequence and serve different audiences:

This provides a high-level view focused on business needs. It’s a simple, schematic representation of data blocks (customers, products, orders) and their relationships — without technical detail.

Main entities are shown as rectangles, connected by arrows. The CDM is used by operations teams, analysts, and project managers to share a common understanding.

This translates the conceptual schema into precise rules. Attributes, data types, and cardinalities are defined.

For example, the customer name field is defined as text, and the order date as a date format. A customer may place multiple orders, but each order is linked to a single customer (1–n cardinality).

This model is used by data engineers and architects to prepare for technical implementation.

This is the technical implementation. It describes the tables, columns, primary keys, and relationships in a database (e.g. a CUSTOMER table with fields like id, name, email).

This model is used by developers and DBAs to deploy the operational structure.

A data model responds to specific business needs. A few real-world examples by sector:

💡 The term Common Data Model (CDM) refers to a shared language that standardises key objects (customers, accounts, products, transactions). It provides a common foundation that makes system integration simpler and more reliable.

Data models first appeared in the 1960s–70s to structure on-premise relational databases managed in closed, local environments.

Today, most organisations use modern infrastructures, with cloud-based data warehouses (BigQuery, Snowflake, Redshift) at the core. These serve as a single source of truth, where data is centralised before fuelling business tools.

The Modern Data Stack, built around best-of-breed components, has reinforced this approach. Modular and collaborative models have emerged, with tools like dbt or Looker/LookML. These simplify the creation, documentation, and maintenance of data models.

Value now lies in the ability to connect and activate data. A well-designed data model enables a composable CDP to tap directly into the warehouse, unifying profiles and powering large-scale activation scenarios.

Traditional CDPs often impose a rigid data model. Businesses must fit into predefined templates — a customer model, a transaction model — with little flexibility for specific use cases.

Unfortunately, these standard schemas don’t suit every reality. A marketplace may need to manage both sellers and end-buyers. Some companies operate in hybrid B2B/B2C models.

With DinMo, you retain full control and we support you in designing the right structure. Our data model, the Knowledge Store, connects directly to your data warehouse. It avoids rigid templates and allows for a customised, adaptive model that fits your business.

The benefits of this approach:

💡 An e-commerce business can automatically segment at-risk customers, personalise follow-up emails, and sync that data with advertising campaigns — with no duplication and maximum agility.

Building a data model is a progressive process with key steps:

📌 Best practices for a successful data model:

Collaborate: involve marketing, sales, IT, and data teams from day one.

Document: keep definitions and rules accessible to all.

Plan for scalability: your model should evolve with new needs and data sources.

Use the right tools: leverage solutions that simplify implementation.

The data model is the foundation of any data strategy. When aligned with your business needs, it guarantees reliable, consistent, and actionable information. It unlocks valuable insights to power your marketing, sales, and operations.

DinMo makes it easy to design and deploy data models tailored to your needs. Our composable approach lets you activate your customer data faster and with greater impact.

👉 Get in touch to see how DinMo can help you turn your data models into a performance driver.