What Is Data Modeling? Examples, Techniques, and Best Practices

We live in a world fueled by data, and companies are collecting more of it than ever. Every day, transactional systems, customer interactions, IoT devices, cloud applications, and digital platforms generate massive amounts of raw information. 

Yet raw data alone doesn’t magically create value. Without structure, consistency, and context, data becomes difficult to trust, hard to analyze, and nearly impossible to scale.

This is where data modeling becomes essential.

Data modeling is the practice of defining how we organize, connect, and govern data within an organization. It provides the architectural foundation that transforms scattered information into reliable data to power analytics, artificial intelligence (AI), machine learning (ML), and confident business decisions.

In this guide, we’ll cover:

• What data modeling is and why it matters for you.
• The core types of data models you’ll encounter.
• Common data modeling techniques you can use.
• How data modeling helps you make smarter decisions with analytics and AI.
• Best practices for successful data modeling.
• Real-world examples and use cases to inspire you.

Whether you’re setting up your first analytics environment or modernizing an enterprise data platform, understanding data modeling is essential for achieving lasting success.

What is data modeling?

Data modeling is the process of creating a formal representation of data objects, their attributes, relationships, and the rules that govern how data is stored, accessed, and maintained within an information system.

Data modeling answers fundamental questions:

• What data should be captured and stored?
• How is data related across business processes?
• What constraints keep data accurate, trustworthy, and consistent?
• How will the data support reporting, analytics, and applications?

The output of this process is a data model: a structured blueprint that guides the design of databases, data warehouses, data lakes, and analytics platforms.

A well-designed data model provides a shared language between business and technical teams, aligning operational requirements with system architecture. It reduces ambiguity, prevents redundant or conflicting data definitions, and establishes a foundation for scalable growth as data volumes and use cases expand. 

By clearly defining how data elements connect across systems and workflows, data modeling also improves data quality, simplifies integration efforts, and speeds up development cycles. Over time, this structure gives organizations a way to respond more quickly to new reporting demands, regulatory requirements, and analytical initiatives, turning raw data into a reliable strategic asset rather than an operational liability.

Purpose of data modeling

Data modeling exists to solve a wide range of technical and business challenges:

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Without a strong data model, organizations often experience broken dashboards, conflicting KPIs, unreliable AI models, and endless rework across data teams.

Types of data models

Data modeling typically follows a layered approach consisting of three primary model types:

• Conceptual data model
• Logical data model
• Physical data model

Each layer serves a distinct purpose and audience, building progressively from business understanding to technical execution.

1. Conceptual data model

What it is:
A high-level business view of data

Purpose:
To define what data matters to the organization and how major entities relate to each other, without technical details

Audience:
Business leaders, domain experts, stakeholders

Characteristics:

• Technology-agnostic
• Focused on business concepts and terminology
• Minimal technical complexity

Example:
A retail organization’s conceptual model might include:

• Customer
• Product
• Order
• Store
• Supplier

And relationships such as:

• A customer places an order
• An order contains products
• A store fulfills orders

This model allows business and technical teams to share a common understanding of central concepts. It’s often the first step in aligning organizational priorities with data strategy, helping leadership articulate how information flows through the enterprise and supports business objectives.

2. Logical data model

What it is:
A detailed, structured representation of data elements and relationships, still independent of specific technologies

Purpose:
To translate business concepts into organized data definitions

Audience:
Data architects, analysts, developers, engineers

Characteristics:

• Defined attributes and data types
• Specified relationships and cardinality
• Documented business rules
• Applied normalization

Example:

Customer

• Customer_ID (Primary Key)
• First_Name
• Last_Name
• Email
• Loyalty_Status

Order

• Order_ID (Primary Key)
• Customer_ID (Foreign Key)
• Order_Date
• Total_Amount

The logical model provides the structural foundation for system design, ensuring consistency across applications and enabling teams to validate requirements before rollout begins.

3. Physical data model

What it is:
The technical use of the logical model in a specific database or data platform

Purpose:
To optimize data storage, performance, and scalability

Audience:
Database administrators, platform engineers

Characteristics:

• Tables, columns, indexes
• Partitioning strategies
• Database-specific data types and syntax

Example:

CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(100),
    last_name VARCHAR(100),
    email VARCHAR(255),
    loyalty_status VARCHAR(50)
);

The physical model is what actually runs in production, translating design decisions into operational systems that support day-to-day business processes at scale.

Common data modeling techniques

1. Entity-relationship (ER) modeling

The most widely used modeling method.

Key components:

• Entities: real-world objects (Customer, Order)
• Attributes: properties of entities
• Relationships: connections between entities
• Keys: primary and foreign keys

ER diagrams visually communicate how data elements relate across the system. They provide a clear, standardized way for both technical and non-technical stakeholders to understand system structure, making them especially useful during requirements gathering, system design, and documentation. By mapping business concepts directly to data structures, ER modeling helps uncover gaps, inconsistencies, and design risks early in the development process.

2. Normalization

A technique for structuring data to minimize redundancy and improve integrity.

Goals:

• Eliminate duplicate data
• Prevent anomalies during updates
• Improve consistency

Common normal forms (NF):

• 1NF: atomic values
• 2NF: full functional dependency
• 3NF: remove transitive dependencies

Highly normalized models are ideal for transactional systems where data accuracy, reliability, and frequent updates are critical. They also simplify maintenance and reduce the risk of data corruption as systems evolve.

3. Dimensional modeling

Optimized for analytics and reporting workloads.

Uses:

• Fact tables: measurable business events (sales, clicks, revenue)
• Dimension tables: descriptive context (time, customer, product)

Common schemas:

• Star schema
• Snowflake schema

Dimensional models support fast queries and intuitive dashboards so people in business can explore trends and performance with minimal technical complexity.

4. Data vault modeling

Designed for large-scale, enterprise data warehouses.

Structure includes:

• Hubs: core business keys
• Links: relationships
• Satellites: descriptive attributes and history

Data vault modeling provides scalability, auditability, and historical tracking, making it well-suited for enterprises operating in complex, rapidly changing data environments where long-term data integrity, regulatory compliance, and traceability are critical.

Analytics enablement

High-quality analytics depend on consistent, reliable data. Data modeling establishes the structural foundation that makes this possible across systems, teams, and reporting environments. By clearly defining entities, relationships, and business rules, data models eliminate ambiguity and make sure that metrics are calculated the same way regardless of who is querying the data or which tool they are using.

Data modeling ensures:

• Common definitions across reports
• Accurate joins and aggregations
• Faster query performance
• Reusable, trusted data sets

Without strong modeling, analysts spend more time fixing data than analyzing it. Instead of discovering insights, teams become trapped in validation cycles, reconciling conflicting numbers and rebuilding logic that should already exist at the architectural level.

AI and machine learning readiness

AI and ML models are only as good as the data they learn from. Data modeling plays a critical role in preparing high-quality training data that’s consistent, complete, and governed.

Data modeling helps:

• Standardize training data sets
• Maintain consistent features across models
• Reduce bias introduced by incomplete or dirty data
• Improve explainability and governance

When features are clearly defined and relationships are well understood, models become more accurate, easier to interpret, and safer to deploy. Well-modeled data also supports ongoing model monitoring, retraining, and compliance requirements as AI systems evolve.

Making better business decisions

Executives rely on metrics such as revenue, churn, lifetime value, and forecast accuracy to guide strategic direction. Data modeling provides the structural discipline required to make sure those metrics are trustworthy and repeatable.

Data modeling provides:

• A single source of truth
• Consistent KPI definitions across the enterprise
• Transparent data lineage from source to dashboard
• Greater confidence in strategic decisions

Strong data models turn dashboards into engines of growth by transforming raw information into dependable insight that leadership can act on with confidence.

Steps to effective data modeling 

1. Start with business goals

Effective data modeling begins with a clear understanding of the business outcomes the organization is trying to achieve. Rather than treating modeling as a purely technical exercise, leading organizations anchor every design decision in strategic priorities such as revenue growth, customer retention, operational efficiency, compliance, and risk management.

When models are built without explicit business alignment, teams often produce technically sound architectures that fail to support them when making decisions. The result is fragmented reporting, underutilized data sets, and frustration across the organization. Starting with business goals ensures that the data captured, structured, and exposed is directly tied to the questions executives and operators are asking.

This process typically begins with stakeholder interviews and workshops to identify key performance indicators, reporting requirements, and future analytical ambitions. From there, modelers can define the core entities, relationships, and constraints that support those objectives. This alignment allows data models to evolve alongside business strategy, rather than lag behind it.

By grounding modeling efforts in business priorities from day one, organizations keep their data architecture relevant, scalable, and capable of delivering measurable value.

2. Engage stakeholders early

Data modeling is most successful when it’s treated as a collaborative effort between technical teams and business stakeholders. Engaging stakeholders early helps ensure that models reflect how the organization actually operates—not just how systems currently store information.

Business leaders, domain experts, analysts, and engineers each bring critical perspectives. Early engagement allows assumptions to be surfaced, definitions to be aligned, and potential gaps to be identified before design decisions become expensive to reverse. It also builds trust and shared ownership of the resulting data architecture.

Workshops, design reviews, and iterative feedback cycles are especially effective for aligning on terminology, business rules, and reporting expectations. These conversations often uncover discrepancies in how different teams interpret key concepts such as “customer,” “order,” “revenue,” or “active user.” Resolving these differences at the modeling stage prevents downstream confusion and rework.

Organizations that involve stakeholders early tend to produce models that are easier to adopt, easier to govern, and far more resilient as business requirements evolve.

3. Document definitions and assumptions

Clear documentation is the connective tissue that keeps data models usable, trustworthy, and scalable over time. Without it, even the best-designed models quickly become opaque and difficult to maintain.

Documentation should capture business definitions, entity descriptions, attribute meanings, transformation logic, constraints, and known assumptions. This creates a shared reference point for analysts, engineers, auditors, and business people, reducing dependency on tribal knowledge and individual memory.

Well-documented models make onboarding faster, simplify troubleshooting, and support compliance initiatives by providing traceability from source systems to downstream reports and dashboards. Plus, when business requirements or source systems change, you’ll find it far easier to see the impact.

Organizations that treat documentation as a first-class artifact rather than an afterthought consistently experience better data quality, faster development cycles, and stronger confidence in analytical outputs.

4. Balance normalization and performance

While normalization is essential for keeping data trustworthy and minimizing redundancy, performance considerations must also guide modeling decisions, especially in environments that support high-volume analytics and real-time reporting.

Highly normalized models are ideal for transactional systems, but analytical workloads often benefit from selective denormalization to improve query speed and simplify access patterns. Striking the right balance requires understanding how data will be consumed, by whom, and at what scale.

Modelers must evaluate tradeoffs between storage efficiency, maintenance complexity, and query performance. In many modern architectures, this leads to hybrid approaches that maintain normalized structures in operational systems while supporting denormalized representations in analytical platforms.

Organizations that explicitly design for both integrity and performance avoid the common pitfalls of overly rigid models that slow insight delivery or overly simplified structures that compromise accuracy.

5. Design for scalability and change

Data models must anticipate growth, not just in data volume, but in business complexity, regulatory requirements, and analytical sophistication. Designing for scalability means building structures that can accommodate new entities, attributes, and relationships without forcing disruptive redesigns.

This requires modular thinking, clear separation of concerns, and careful management of dependencies. Techniques such as abstraction layers, schema versioning, and extensible modeling patterns help organizations evolve their data architecture without breaking existing systems and reports.

Equally important is designing for change. Business models shift, product lines expand, and regulatory frameworks evolve. Data models that are too rigid quickly become obstacles rather than enablers. Organizations that plan for change upfront build data platforms that remain valuable long after initial rollout.

6. Prioritize data quality and governance

High-quality data is the foundation of trust in analytics, AI, and business decision-making. Data modeling plays a central role in enforcing quality standards by defining constraints, validation rules, and governance structures directly into the architecture.

Effective governance includes clear ownership of data domains, defined stewardship responsibilities, consistent naming conventions, and documented approval processes for change. When governance is embedded into modeling practices, organizations reduce errors, improve compliance, and create stronger accountability for data assets.

This discipline also supports regulatory requirements, audit readiness, and risk management by ensuring sensitive data is handled appropriately and that reporting is defensible under scrutiny.

7. Continuously review and refine models

Data models are living assets, not one-time deliverables. As business conditions, technologies, and analytical needs evolve, models must be revisited, tested, and refined.

Regular reviews help identify outdated assumptions, unused structures, performance bottlenecks, and opportunities for simplification or enhancement. Feedback from analysts, engineers, and business people provides critical information into how well the model is serving its purpose.

Organizations that treat continuous improvement as a core modeling principle maintain data architectures that remain aligned with strategic objectives, support new ideas, and sustain long-term value.

Why Domo

Data modeling is more than a routine technical task; it’s the strategic discipline that determines how well organizations use their data. With data volumes soaring and AI adoption on the rise, strong data modeling becomes the foundation of sustainable analytics, novel ideas, and your competitive advantage.

Domo helps organizations put data modeling to work at scale by connecting data across the enterprise, enforcing consistent definitions, and turning complex information into trusted, decision-ready insights. With Domo’s cloud-based platform, teams can model, integrate, govern, and analyze data in one place, breaking down barriers and delivering faster results.

Whether supporting executive dashboards, advanced analytics, or AI initiatives, Domo provides the visibility, flexibility, and governance for data models to align with your business goals as your company grows and evolves.

Contact us to learn how Domo can help your organization build stronger data foundations, discover deeper insights, and turn data into your durable competitive advantage.