Data modeling is the foundation of reliable analytics. It defines how data is organized, connected, and made useful — not just for storage, but for decision-making. Whether you’re designing a new warehouse or adapting to ever-changing source systems, a strong data model ensures consistency, scalability, and trust.

In this guide, we’ll break down what data modeling is, how it works, and the key approaches modern teams use to structure data for long-term success.

What’s data modeling?

Data modeling is the process of defining how data is structured, related, and stored within a system. It creates a visual blueprint that outlines how data moves through a database, including how it’s grouped, organized, and accessed. Effective data models are critical for building scalable BI applications and reliable reporting systems.

Broadly speaking, data models fall into two categories: operational and analytical. Operational models map out how data flows in day-to-day applications, while analytical models provide additional context to data, like reporting and analysis tools for workloads.

A strong data model clearly defines relationships between entities, reflects business logic, and provides a shared understanding across technical and business teams. It’s the foundation for trustworthy, high-impact data use.

Why is data modeling important?

Although data models are mostly used when planning out new BI applications or information systems, they aren’t a one-time asset. The best models will grow and evolve as your company does, becoming shared references to help everyone understand where data comes from and how to organize it.

From a technical standpoint, data models also decrease redundancies. Clearly plotting a system allows you to flag constraints or spot optimization opportunities ahead of time. When you actually build out the data architecture, the model guides you toward making an efficient structure from the start.

And if you ever need to add a new system, you’ll be able to use existing data model assets to see how they integrate with your existing architecture. 

Types of data models

There are three main types of data models: physical, logical, and conceptual. Each has varying degrees of detail and abstraction — here’s a closer look at the differences.

Physical

Physical data models describe how you’ll store data within databases or data warehouses. For relational data, this means you’ll plot out the tables, columns, rows, data types, and indexes. After putting those building blocks in place, you’ll add primary and foreign keys to show any underlying relationships and integrity rules.

Logical

A logical model focuses on the structure of data itself. It goes into depth about data types, relationships, attributes, and business rules, but avoids database-specific details like indexing strategies. Logical models help maintain consistency across systems and provide a structured blueprint for implementation.

Conceptual 

A conceptual data model focuses on the big picture of how entire data systems work. It outlines high-level entities and their relationships without covering attributes or any technical constraints. Most of the time, conceptual models are what you would show to non-technical stakeholders to help them understand how data runs through your business.

Data modeling processes

Building a data model is an iterative process that needs input from both business and technical teams. Here’s how you translate business requirements into a structured model.

Step 1. Identify business requirements

First, you need to understand the purpose of your data model. What questions does it need to answer? What workflows does the data support? Who will use this data? Answering these questions aligns your model with business needs and ensures it’s driven by real-world utility.

Step 2. Define data entities

Identify the main entities that you’ll use in the model and what makes them unique. For example, in a product analytics model, your entity might be “Product.” Its attributes would be product ID, price, category, and so on. You’ll want to define what data exists and what fields you need to map it out properly.

Step 3. Develop data models

After pinpointing your entities, it’s time to draw up a conceptual model. Starting with a high-level overview lets you plan out the scope of your data system. You can then move to a logical model to define structure, rules, and relationships in more detail. Finally, create a physical model to show how the system will work on a technical, data-specific level. Each layer of detail adds specificity and clarity, reducing friction when it comes time to implement.

Step 4. Validate data models

Take your finished models to the stakeholders, giving business teams the chance to assess whether the conceptual model meets what they had in mind. Your technical team can inspect your logical and physical models for accuracy and feasibility. Use any feedback to iterate on your designs and ensure your models are consistent and useful for end users.

After a few rounds of feedback, you should have a well-aligned data model you’re ready to bring to life.

Data modeling techniques

Businesses often work with complex, interconnected, or semi-structured data that requires different organizational structures. Data modeling techniques provide those structures, helping teams represent real-world relationships in a way that’s easy to understand and technically efficient.

Hierarchical data modeling

Hierarchical data modeling organizes information in a tree-like structure. Every record has one parent from which multiple children branch off. Regular data fits neatly into a hierarchical model with its clearly defined top-down relationship. For example, when depicting a company’s structure, one parent may be Marketing, while the branches below might be individual teams or roles within that department.

Although this data model technique is straightforward, it’s very rigid to work with. If you’re using more complex or non-relational data, fitting things into neat parent-child categories doesn’t scale well.

Relational data modeling

Relational data modeling is an extremely common choice for structured data as it organizes information into tables. Different tables are then connected by primary and foreign keys, making it consistent and stable for querying downstream.

Modern data infrastructure makes relational modeling much easier to maintain at scale. Tools like Fivetran automatically replicate source data into cloud warehouses in a structured, analytics-ready format, making relational modeling easier to scale and maintain.

Graph data modeling

Graph data modeling stores data in nodes and edges, representing entities and the relationships between them. Instead of the rigid tables used in a relational database, graph databases attempt to model real-world relationships between data. Visually, this looks like a web of connected points. In practice, graph modeling is useful for complex systems where showing the relationships between data is just as important as the data itself.

Advantages of data modeling

Well-designed data models provide a range of benefits for both technical and business teams:

• Faster, more accurate reporting: By defining entities, relationships, and attributes, a clear data model reduces confusion about where data resides and how users should query it.
• Lower development and maintenance costs: A clear, concise model removes guesswork when building data architecture, helping to reduce the time engineers spend creating and iterating databases.
• Better documentation and compliance: When it’s time for an audit, data models are the perfect reference point for tracking lineage and seeing where you enforce policies.
• A stable foundation for business rules: Data models ensure everyone references the same data and rules when making decisions, bringing consistency to team interactions with data.

How Fivetran powers modern data modeling

Messy, siloed, or inconsistent data makes building reliable models feel like an uphill battle. Fivetran streamlines the process by automatically extracting, loading, and delivering clean, analytics-ready data to your cloud warehouses.

With a reliable, up-to-date data foundation, you can hit the ground running with advanced analytics modeling instead of spending time wrangling raw data. 

Accelerate your data modeling with confidence by exploring Fivetran’s automated ELT platform — sign up today to get started for free.

FAQs

How can I create data model examples?

Begin by outlining your main business requirements and what needs your data fills. You can sequentially add details to your plan with a conceptual design, include logical data flow information, then design the physical infrastructure of your databases.

What are some data modeling tools?

Data modeling tools include SQL-based modeling platforms, data build tools (DBT), graph modeling databases, and cloud ELT solutions like Fivetran that help automate data delivery into your models.

Is data modeling part of ELT?

No — data modeling is the process of structuring data for analysis, while Extract, Transform, Load (ELT) refers to the pipeline that moves and prepares data for use. ELT and data modeling work together: ELT delivers raw data to your warehouse, and data modeling organizes it into usable formats for analytics.

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