What is data modeling and why do you need it?

Data modeling is a critical component in the development of information systems, enabling efficient data organization and management. While the advent of Big Data, self-serve and decentralized technologies led to a temporary shift away from traditional data modeling, the tide is turning back. Organizations are rediscovering the efficiency and effectiveness of robust data modeling practices. This resurgence is driven by the many benefits of data modeling, such as significantly improved data quality, greater business agility, increased productivity, and streamlined data maintenance. These benefits underscore the indispensable role of data modeling in creating scalable, efficient, and reliable information systems and data solutions.

Why does data modeling matter?

Organizations that efficiently collect, analyze, and utilize data gain a significant competitive advantage. Data modeling is important because it plays a pivotal role in this process, structuring and organizing data to work effectively and extract relevant information to support business processes.

Data modeling is like a map for you database. Visualization of when you use data modeling tool you can have a big picture of your database.
It is like a map for your database

The neglect of data modeling resulted from rapid technological advancements and a lack of understanding of its value, seen as an unnecessary complication by many data engineers. The problem was driven by the rapid evolution of technology. New database technologies and tools promise fast and easy data storage without the need for complex modeling.

Today, however, the growing volume of data and the complexity of data environments have made data organization even more critical, bringing data modeling back to the forefront. Modern systems that deal with data from multiple sources increase the need to integrate and manage data. Data models serve as the foundation for advanced analytical tools and techniques.

This article dives into the world of data modeling and explores its importance to modern information and data management systems. We'll discuss what data modeling is, its benefits, and why it's becoming increasingly important. You'll learn how data modeling improves data quality, increases flexibility and productivity, and simplifies information system maintenance. Learn about the entity-relationship diagram and its levels. Whether you're an experienced data engineer or new to data analysis, this article will provide valuable insight into this key aspect of modern software engineering.

What is Data Modeling?

Data modeling is the process of designing a data solution in terms of data storage. Essentially, it visualizes the design of a database or application. Data models serve as the foundation for designing and implementing databases, facilitating efficient data storage and management. They ease the integration of data from different sources, supporting data consolidation and centralization for comprehensive analysis. Simplifying access to and analysis of data, models enable information availability to various users, supporting data-driven decision-making. Data modeling is a component of data management.

Types and Levels of Data Modeling

There are countless types of data models, but this article focuses on the most common: the entity-relationship diagram. Regardless of the model type, there are three levels of models:

  • Conceptual  
  • Logical
  • Physical
Levels of data modeling. From the conceptual to the logical to the physical. From the more abstract to the more specific.
Levels of data modeling

Conceptual modeling is typically done at the beginning of the database design process.  

Logical and physical models build on the conceptual model, developing it in greater detail.  

All three models are crucial for a comprehensive understanding of data structure and their database implementation.

Main differences between levels of data modeling. Their focus, details and aim.
The main differences between the different levels of data modeling

Understanding Entities and Relationships in Data Modeling

Before we dive into the intricacies of conceptual, logical, and physical models, it's essential to grasp the foundational concepts of Entity and Relationship in data modeling.

Entity Simplified

An entity in data modeling represents any distinct object or concept for which information is gathered. This could be anything from a customer, product, to an order, embodying both tangible and intangible elements. In the database, each entity translates to a table where details about these elements are stored as individual records. For instance, a "Person" entity may store information such as names, hair color, height, and birth year, with each attribute capturing a specific piece of data.

Relationship Explained

A relationship, on the other hand, defines how entities are connected to one another, illustrating the real-world associations between them. It's the glue that holds entities together, allowing us to model complex interactions within our data. For example, the relationship between "Employee" and "Sales Branch" entities could indicate where each employee works, highlighting a natural connection in the business environment.

The Essence of Entity-Relationship Modeling

The discussion of entities and their relationships naturally leads us to entity-relationship (ER) modeling. This method is crucial for visualizing the structure of database systems, laying the groundwork for creating relational databases. ER modeling is the first step in designing a database, followed by logical and physical modeling, which further refine and implement the conceptual design into a functional database system.

The entity-relationship (ER) model is fundamental to relational database design. It outlines entities (tables) and their relationships (connections), providing a conceptual blueprint for database organization. This model guides the development of relational databases from the initial design to physical implementation, ensuring data integrity and facilitating complex queries. Thus, the ER model is intrinsic to the structure and functionality of relational databases.

We will describe more on the ER modeling topic in the follow-up article and explain details about relation cardinality or different data modeling notations and styles.  

Let's now create a sample data model and explain the conceptual, logical and physical model concept.

Consider that we have a ceramic tableware business and we have several stores in different cities. We need store sales data so that we can realize:

  • sales performance analysis over sales branches
  • forecasting demand
  • employee performance tracking  
  • analyze product sales trends.

Conceptual model

Let's start with the conceptual model. It represents the highest level of abstraction in data modeling. It focuses on the essence and key concepts of a given system without considering implementation details. It provides a shared vision for all stakeholders and facilitates understanding of the data structure at the highest level.

For our sales database that we are modeling. The first entity will be employees, who will be in some relationship with branches. Each employee works at a branch. The branch entity is related to the sales entity, and the sales entity could be linked to the sales time entity or the product entity, for example.

Conceptual model example. Visualization of database. Data from branch has a relation to entity employee, employee to sale and sale to product.
Conceptual model example

For our sales database example we have following breakdown of the entities:

  1. Employee: Represents individuals working for the company.
  2. Branch: Represents physical or logical locations where sales activities take place.
  3. Sales: Represents transactions where products are sold to customers.
  4. Product: Represents the goods or services sold by the company.

The relationships:

  • Employee to Branch Relationship: An employee is associated with a specific sales branch.
  • Sales to Employee Relationship: Sales records are linked to employees.
  • Sales to Branch Relationship: Each sale is associated with a specific sales branch.
  • Sales to Product Relationship: Each sale involves one or more products.

This conceptual model provides a high-level overview of the data in the sales database. It helps us to understand the different types of data that are stored, as well as the relationships between them. This information can be used to design the database, as well as to develop applications that interact with the data.

Logical model

The next step in data modeling is the logical model. Here, we define the logic for storing data, including the structure of columns and their relationships.

The logical model focuses on the conceptual representation of data, independent of technologies and implementation. It is more detailed than the conceptual model and defines specific data structures. However, it still does not deal with the technical details of database implementation.

The logical model provides a formal description of data and its relationships that is understandable for data analysts and developers.

Logical model example
Logical model example

In our example:

  • We will convert the entities into tables or groups of tables.
  • The Employee table (entity) will have attributes (columns) such as Name, Surname, and foreign key column Branch ID that references the Branch table.  
  • The Branch entity, to which the Employee is related, will have the attribute Branch Name.
  • Product information can be split into separate tables, such as 'product' and 'product category', with their own attributes.
  • The 'Sales' table will consist primarily of foreign keys from other tables in the model and attribute Time to store date and time of realized sales.
  • All tables are linked by primary key-foreign key relationships which is an implementation of the relationships from the conceptual model.

The logical model is an important step in data modeling. It helps us to ensure that the data is stored in a way that is efficient and easy to understand. It also helps us to avoid data redundancy and inconsistencies.

Physical model

The physical model is a practical extension of the logical model that includes technical details. It focuses on the technical implementation of the logical model in a specific database technology.

Physical model example
Physical model example

The physical model addresses specific technological questions, such as: “How will data be stored in the specific technology?”.

On the physical model, we create rules. For example, we specify that the ID field must always have a value. We can also limit the number of characters, or specify the type of values that can be stored in a field (text, numeric, etc.). For example, we can specify that the name field can only be filled in with text values.

The completed physical model is then passed to the database developer, who uses it to build the database.  

It is designed with a focus on optimizing database storage capacity and performance. It incorporates security and scalability considerations to ensure that the database meets current and future needs. It also fine-tunes data structures to improve efficient data access and retrieval. It also adapts the logical model to the specific features and limitations of the chosen database.

The physical model defines:

  • Tables
  • Columns
  • Data types for each column
  • Primary and foreign keys
  • Other database properties
  • Rules for data entry

The physical model is the final step in data modeling. It is used to create the actual database that will be used to store and manage the data.

Benefits of Data modeling  

Data modeling provides transparency into the development process, serving as the best documentation of the underlying database. Without a model, mapping database relationships is challenging.  

Data Modeling's Role in Data Governance and AI Integration

Data modeling is crucial for effective data management, enhancing Data Governance, and facilitating artificial intelligence (AI) advancements. It simplifies the complex data landscapes of modern organizations, providing a clear representation of data flows and classifications. This simplification is vital for understanding, managing, and making strategic decisions about data.

In the dynamic realm of data, continuous updates to models are essential, a practice embedded within Data Governance to ensure accuracy and current relevance. Data models also support Data Governance by identifying critical data objects and their interrelations, forming the backbone for solution development and strategic data inclusion.

Moreover, as we venture further into the era of AI, the structured data provided by data modeling proves invaluable. It optimizes data for AI applications, fostering innovation and competitive advantage. Thus, data modeling stands at the intersection of efficient data management, robust Data Governance, and the effective use of AI, driving forward the data-driven imperatives of contemporary organizations.

Petr Mikeška
Dawiso CEO
Petr Mikeška
Dawiso CEO

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