What Is a Data Model?

Growing companies generate mountains of data, such as customer records, sales figures, and supplier details. Without a formal structure, this information becomes chaos, affecting a business’ ability to efficiently use that data.

Data models bring order to the chaos. They organize your information, reveal important connections, and help transform raw data into actionable insights.

Keep reading to learn how data models work, the different types, and their benefits to your business.

What Is a Data Model?

A data model is an abstract framework representing data elements and their relationships, reflecting an organization’s rules, regulations, and information system structures.

It determines what data exists, how it is organized, and how it can be accessed, prepared, and used. While there are different types of data models, they all share the same fundamental goal: to provide a structured and consistent way to manage information.

Types of Data Models

Three main types of data models that typically follow this sequence: Conceptual → Logical → Physical.

1. Conceptual Data Model: Think of this as a simple drawing or a basic map that shows how everything connects. This model helps non-technical people understand the big picture by showing key entities (e.g., “Students” and “Classes”), relationships between those entities, and basic rules that guide how the data connects.

2. Logical Data Model: The logical model integrates additional details to our simple drawing, like adding street names and building numbers to a map. This includes Attributes for each entity (e.g., student names, grades, IDs), defined relationships and rules for linking data, and constraints and guidelines for valid data.

3. Physical Data Model: This part is where the actual building happens. The physical map defines precisely how to store information in the database, including tables, columns, and their names, data types (e.g., integers, text), indexes and optimization techniques, and security settings for data protection.

What is the Data Modeling Process?

Here are all the steps involved in the data modeling process in sequence:

1. Requirements Gathering: Engage stakeholders to understand the purpose of the model and identify essential data. Gather detailed insights on how the data is used and prioritized.
2. Entity Identification: Define the core entities the model must represent, such as people, objects, or events. Identify the attributes that provide context for each entity.
3. Relationship Mapping: Establish how entities are connected, documenting the interactions and dependencies that shape data flow and organization.
4. Attribute Definition: Detail the specific properties of each entity, ensuring all relevant characteristics are captured.
5. Normalization: Structure attributes logically to minimize redundancy and maintain clarity, dividing data into related tables where necessary.
6. Constraint Specification: Set clear rules and restrictions to ensure data consistency, accuracy, and reliability.
7. Model Validation: Collaborate with the team to review the draft model, verifying that it meets requirements and aligns with business goals. Refine based on feedback.
8. Documentation: Create detailed records of the model’s design, rules, and intended use to guide future implementation and updates.
9. Implementation Planning: Develop a comprehensive plan for deploying the model, including coding, data migration, and system testing.

Advantages of Data Models to Businesses

Without a data model, a business may struggle with inconsistent data, redundant storage, and even unreliable reporting. To avoid those outcomes, here are some roles a data model plays in your business:

• Reduces Development Costs: Planning data structures upfront avoids expensive rework later.
• Accelerates Time-to-Market: Clear data relationships speed up development and launches.
• Improves Data Accuracy: Enforces validation rules to minimize errors and inconsistencies.
• Simplifies System Integration: Standardized data structures connect systems easily.
• Enhances Data Security: Defines rules to protect data from unauthorized access.
• Drives Business Intelligence: Creates unified data views for better analytics and insights.
• Strengthens Compliance: Ensures regulatory compliance and simplifies audits.
• Improves Collaboration: Aligns IT, business, and compliance teams on shared models.
• Saves Storage Space: Prevents redundancy to reduce costs and optimize resources.

Use Boomi DataHub for Superior Data Modeling

You can organize your company’s information more effectively, making it easier to understand and use. Following a proper modeling process brings teams together to map connections. Well-structured data saves money, improves decision-making through analytics, ensures accuracy, and drives innovation.

You can use Boomi DataHub to structure your information better. The platform simplifies the management of complex records and ensures your data is of high quality and properly managed.

Here are some key things Boomi DataHub does for you:

• Data Synchronization: Boomi DataHub acts like a sync manager for you. The system ensures your information matches across different systems. Boomi efficiently does this by storing pointers instead of massive datasets.
• Golden Records Creation: Boomi’s platform brings together information from different sources. The model then creates a super-accurate customer or product profile.
• Near-Instant Updates: This solution helps you access up-to-date info whenever you wish.
• Multi-Domain Management: With Boomi DataHub, you can manage customer, employee, and product details. This feature improves how you govern and handle records.
• Smooth Integration: The platform can easily connect to various tools like Salesforce and NetSuite. Easy integration makes your data work more efficiently across platforms.
• Improved Data Quality: Boomi’s system spots inconsistent and duplicate records and cleans up so that you have precise, trustworthy data.

Get the Data Liquidity Index Study to see how businesses are managing the data landscape.