What is a Data Model?

Benefits of Data Modeling

Data modeling’s overarching benefit is to set up a data strategy for success by forcing designers to think through what the real data problem is. It begins with a data model structure that will maximize the advantage of its data by accurately and fully describing data and its flows. Achieving this, the following benefits will follow.

• Avoid and reduce errors in development by addressing large design challenges before implementation
• Improve documentation consistency through accurate and comprehensive definition
• Increase performance by thoroughly planning robust models implemented well
• Reduce the burden of data mapping
• Improve collaboration and communication between technical and business personnel
• Accelerate the process of database design at all three data modeling levels

Types of Data Models

Data modelers rely on three models, or views, of the data model to help abstract models for understanding the structure of their data.

• Conceptual Model: Conceptual models begin with overall content as inspiration for the structure of the data model. The concept informs the definition of the data structures and the entities that will form foundational elements. The conceptual data model’s focus is on entities, their characteristics, and relationship between them.
• Logical Model: The logical model represents the elements described in the conceptual model with greater technical detail, like defining data structures, and details on keys, data types, and attributes. These details do not include technical specifications for any database. At this stage, the logical data model can be used as a blueprint to build the data structures in any database product.
• Physical Model: The logical model is then translated into a physical model of the database application. The physical model specifies a blueprint fit for the implementing database.

Data Modeling Structures

Hierarchical Data Model

The hierarchical data model is arranged in a treelike configuration of parents and children. Children have only one parent. The model forms a one-to-many relationship, meaning that there exists for each element only one unique path to access it.

Relational Data Model

The relational data model would supplant the hierarchical model. It is configured into tables with columns recording attributes, and rows as entities. Relational databases can support one-to-many, one-to-one, and many-to-many relationships.

Dimensional Data Model

Dimensional data models are used for data warehouses or data marts because they support business intelligence. They consist of two types of tables, fact tables that contain data about transactions and events, and dimensional tables that list attributes about those entities in the fact table. These models are used to increase speed.

Entity-Relationship (ER) Data Models

Similar to relational data models, entity-relationship data models are closely modeled on reality rather than the underlying database. From the ER data model a relational data model can be produced.

Object-Oriented Data Models

Object-oriented data models describe models as objects with class hierarchy and inheritance of attributes. This approach is good with multimedia and hypertext databases.

Steps in the Data Modeling Process

The data modeling process consists of six steps outlined by Peter Aiken, associate professor of information systems, at the 2019 Dataversity webinar.

1. Identify the business entities that are represented in the data set.
2. Identify key properties for each entity to differentiate between them.
3. Create a draft entity-relationship model to show how entities are connected.
4. Identify the data attributes that need to be incorporated into the model.
5. Map the attributes to entities to illustrate the data’s business meaning.
6. Finalize the data model and validate its accuracy.

A seventh step should be placed at the end, to continuously revise and update the data models as the data assets and business requirements and needs shift.

Data Modeling Examples

Data modeling is a critical foundational step in the design and development of any application, whether it’s for business, entertainment, personal use, or any other domain. It plays a vital role in shaping the structure and infrastructure of the system by organizing how data will be stored, processed, and accessed. This applies to all types of systems, including transactional applications, data processing platforms, and any environment that collects, generates, or utilizes data.

In particular, data modeling is essential for data warehousing, as data warehouses aggregate information from multiple sources, each of which may store similar or related data in different formats or structures. Before this data can be effectively stored or analyzed, it must be standardized and aligned to a unified warehouse schema. A well-constructed data model provides a blueprint for how incoming datasets should be transformed, mapped, and integrated to fit the warehouse’s design. Without this structure in place, it would be nearly impossible to ensure consistency, accuracy, and usability across the consolidated data.

Once established, the data model serves as a backbone for advanced functions such as analytics, data mining, executive dashboards, and cross-system integrations. It provides a clear, logical structure that enables seamless interaction between applications and analytic tools. In this way, the data model not only improves operational efficiency but also enhances strategic decision-making by enabling more accurate and timely insights.

During the early planning phases of any system development, data modeling serves as a critical prerequisite. It lays the groundwork for all future development efforts, ensuring that every function, feature, or integration has a consistent and reliable data structure to build upon. Think of it as a universal language for data that ensures coherence and interoperability across systems and platforms.

This importance is magnified in today’s increasingly complex digital landscape where big data, AI, machine learning, cloud-native applications, IoT, and distributed systems like edge computing are rapidly becoming the norm. In such environments, having a strong data model isn’t just beneficial, it’s indispensable. It ensures systems can understand and exchange data accurately, regardless of their origin or architecture, making it a cornerstone of modern, data-driven innovation.

Data Model Tools

Data model tools support the process of defining how data is structured, stored, and related in databases, data warehouses, or other information systems. They help users including data architects, analysts, developers, and business stakeholders design logical, conceptual, and physical data models efficiently and accurately.

You should consider using a data modeling tool when:

• You’re starting a new system that requires a clear data foundation.
• You’re migrating data between systems or modernizing a legacy database.
• You need to enforce data governance policies or compliance standards.
• Your organization is scaling and needs a centralized view of enterprise data.
• Collaboration is needed between technical and business teams on data design.

By offering the ability to visualize, validate, and communicate data structures, data model tools support better decision-making, system performance, and business alignment. Choosing the right tool depends on your team’s technical expertise, infrastructure, and specific use case, from traditional relational databases to modern cloud-based data platforms.

Data Modeling FAQs

What is data modeling?

The process of design and drafting a visual diagram of a system, software package, or database. It should define connections, data attributes, and flows using text, symbols and lines.

What are the types of data modeling?

The three commonly used data models are relational models, dimensional models, and entity-relationship models. Lesser used models are hierarchical, object-oriented, multi-valued, and network.

What is the data modeling process?

The six steps in the data modeling process are:

1. Identify the business entities that are represented in the data set.
2. Identify key properties for each entity to differentiate between them.
3. Create a draft entity-relationship model to show how entities are connected.
4. Identify the data attributes that need to be incorporated into the model.
5. Map the attributes to entities to illustrate the data’s business meaning.
6. Finalize the data model and validate its accuracy.

Why is data modeling important?

Data modeling is a collaborative planning process that is absolutely imperative to ensure the data model accurately reflects its intended use. Inaccuracies can lead to flaws in the database design and lead to further errors and complications.

What are the three levels of data abstraction?

The three layers of data abstraction are the concept layer, the logical layer, and the physical layer. The concept layer inspires the database by defining at a high level the elements to be modeled. The logical layer adds more detail, and is like a master blueprint that can be used to develop the model for a database. The physical layer model demonstrates the translation of the logical layer into the strict requirements of a database. Attributes identified on the logical layer, are now fully defined on the physical layer.