Data Modeling Types and Techniques

Gaurav Singh

December 13, 2023

Data modeling turns raw data into a more easily understood visual representation. 

Businesses generate vast amounts of data. In its raw form, all that data is nearly impossible to understand. 

Data modelers make charts, graphs, and other simplified visuals that help make sense of complex information. These data modeling techniques help users identify and understand the relationships between different pieces of data. 

Data models help business decision-makers and other data consumers see the current health of the business, forecast demand, estimate financial performance, and simulate different scenarios to determine which course to take.

Data modeling can help to increase data consistency by ensuring that data is consistent across different applications and systems. It can help identify and correct data errors, inconsistencies, and redundancies. This ensures that your data is accurate, consistent across the organization, and can be relied upon for decision-making.

Data modelers’ work makes it easier for users to find and access the data they need, which in turn helps improve their productivity. 

In short, data modeling helps create the ‘single source of truth’ businesses need for effective operations and regulatory compliance. 

This article examines the types and techniques of data modeling. 

Types of Data Modeling

Data engineers and data processing rely on many data modeling techniques with different possible presentations. At the core of these various techniques are three concepts of modeling: conceptual, logical, and physical.

Conceptual Data Models

Conceptual models show the “big picture.” That is, they represent the overall structure and content of the data plan but not the details. Data modelers use them as the first step in identifying the data sets and mapping the organization’s data flow. The conceptual model acts as the high-level blueprint for developing the logical and physical models. Projects that are limited in scope (data from a single application, for example) may not require a conceptual model.

Logical Data Models 

The second step is the logical data model. It describes the database content and data flow mapped out in the conceptual model, adding detail to the overall structure. A logical data model does not include specifications for the database itself, however, because the model can be applied to various database technologies and products—relational, columnar, multidimensional, NoSQL, and even an XML or JSON file structure. Those specifics are described in the physical data model.

Physical Data Models

The physical database model contains the details that allow data engineers to build the database structure out of hardware and software. It is specific to the business’s designated database software system and applications. If different database systems are used, a single logical model may require multiple physical models.

Data Modeling Techniques

Three data modeling techniques are commonly used today: dimensional, entity-relationship, and graph data modeling. Their predecessors—hierarchical, relational, and object-oriented data modeling—are still viable options, however.

Dimensional Data Modeling

Dimensional data modeling is often used for business intelligence (BI) and analytics applications. It involves designing star or snowflake schemas. 

The star schema technique is optimized for large data set queries. It organizes data in a database so it is easy to understand and analyze. Star schemas work well for data warehouses, databases, data marts, and other databases. They reduce the duplication of repetitive business definitions, which accelerates data aggregation and filtering in the data warehouse.

snowflake schema is an extension of a star schema that breaks down dimension tables into logical subdimensions. They are often used for BI and reporting in OLAP data warehouses, data marts, and relational databases. The appeal of snowflake schemas for analysts is that it makes the data model easier to work with.

Entity-Relationship Data Modeling

An evolution of the relational model, entity-relationship (ER) data modeling is a high-level technique based on how entities, their relationships, and attributes all connect. In this technique, entities represent core business objects, events, or functions (customers, accounts, and products, for example). Attributes are the columns that describe an entity, and relationships are the actions or attributes that join entities together. 

ER modeling focuses on the processes of a business, with the goal of creating a relatively compact architecture based on the organization’s entities and events (procedures). It’s good for showing how tables connect and for understanding database architecture at a higher level. The downside of this technique is that it doesn’t define long-term database structure very well. Data engineers often take what they learn from an ER model and create more complex, meaningful, and scalable data model architectures.

Graph Data Modeling 

Unlike traditional data models that prioritize data within tables and columns, graph data modeling focuses on capturing the relationships between entities. Sometimes referred to as network data modeling, this technique creates interconnected nodes, in which each node represents an entity connected by its relationships with other entities. 

Relationships between nodes can be directed (one-way) or undirected (two-way) and have specific types or labels. Nodes and relationships also have properties that provide additional information. Properties can be names, dates, or locations for nodes and information such as weight or duration for relationships.

Graph data models are excellent for exposing complex relationships that might be hard to see in traditional data models. They are often used in fraud detection, social network analysis, and recommendation systems. They’re also highly flexible and scalable, allowing for powerful and intuitive querying that is impossible in other data models.

Hierarchical Data Models

Hierarchical data models represent data in a tree-like structure, where information is organized in levels with parent-child relationships. A simplified example is a family tree: the highest level is the parent, followed by branches for children, then grandchildren, and so on. Each element in the hierarchy is called a node, and connections between them are called links.

Hierarchical data models excel at representing data with a natural parent-child hierarchy, providing an intuitive and efficient way to organize, store, and access information. While their limitations should be considered, they remain a valuable tool for managing data in various scenarios with well-defined levels and relationships.

Relational Data Models

Relational data models, the foundation of relational databases (RDBMS), offer a structured and organized way to represent and manage data. They organize data into relations, also known as tables, where each relation holds records (rows) related to a specific entity or concept. 

Relational data models provide a well-established and versatile approach for managing data, particularly for structured and well-defined information. Their advantages make them a popular choice for a wide range of applications across various sectors.

Object-oriented Data Models

Object-oriented data models (OODMs) offer a unique perspective on organizing and managing data, taking inspiration from the principles of object-oriented programming (OOP). Unlike relational or hierarchical models, OODMs focus on representing data as objects, embodying both data itself (attributes) and the behaviors associated with that data (methods).

OODMs are similar to graph data models. The main difference is that OODMs focus on individual objects and their internal coherence, with relationships emerging through object interactions. Graph data models, on the other hand, focus on the explicit relationships and connections between entities, presenting an overall picture of interconnectedness.

Object-oriented data models provide a powerful and flexible way to organize and manage complex data, particularly when data exhibits well-defined entities and behaviors. While they might have their challenges in terms of complexity and adoption, their strengths make them valuable tools for specific data management scenarios and software development.

The Value of Data Modeling

The value of data modeling to a business is immense and multifaceted. The insights gained through effectively visualizing raw data can impact several critical aspects of an organization’s operations and performance. 

Data models provide a structured and organized framework for storing and accessing data, making understanding the relationships between different data points easier. A well-designed data model enables efficient querying and analysis, allowing businesses to unlock valuable insights from their data. This leads to better decision-making across various areas, from marketing and sales to finance and operations.

Certain data models can be used for predictive analytics, helping businesses anticipate future trends and make informed decisions proactively. This can be crucial for managing inventory, optimizing pricing strategies, or assessing potential risks.

By understanding their data better, businesses can optimize processes, identify inefficiencies, and make faster, data-driven decisions, leading to increased agility and responsiveness to market changes.

Overall, data modeling is a fundamental investment for businesses of all sizes. It serves as the foundation for effective data management, analysis, and decision-making, ultimately contributing to increased efficiency, competitiveness, and innovation. 

The long-term value of data modeling far outweighs the initial investment in design and implementation, making it a strategic asset for any organization.