Data Modeling – Overview, Concepts, and Types

Data modeling is creating visual representations of data structures to define how Data is stored, connected, and used within a system. It helps in designing complex databases while ensuring greater data consistency.

Data is revolutionizing the way the world functions in the current day. Data is essential in all fields, whether it is about discovering novel treatments for illnesses, increasing a business’s earnings, or providing you with tailored advertisements on social media.

In essence, this data is readable and processed by machines. However, do you believe that businesses can benefit from all of the data that is available online? A product team, for instance, cannot benefit from client data unless it is connected to particular product transactions.

Data modeling comes into play in this situation. The process of arranging data into a logical framework is known as data modeling. It transforms intricate data into actionable insights that businesses may utilize to guide their actions.

What Is Data Modeling In Software Engineering?

In software engineering, data modeling is the process of more simply expressing and arranging data structures. It represents data and demonstrates how it relates to other data using text, symbols, and diagrams. When designing the structure and use of data in a system, this procedure is a crucial first step.

The process of data modeling entails building data models that show the relationships between data. Eventually, databases and outdated software systems are updated using these models. Businesses may guarantee uniformity in the naming, organizing, and security of their data by employing data modeling, which eventually enhances their data analysis procedure. Whatever the application, the primary goal is to ensure that the team has access to well-organized data.

Types Of Data Models

A database or information system’s design process begins with a basic concept and advances to more specific details over time. Based on their level of complexity, data models—which depict the structure of the data—can be divided into three categories: conceptual, logical, and physical.

A conceptual model usually leads the way, followed by a logical model and a physical model at the conclusion. With the aid of the graphic provided below, let’s examine each of these models in more detail:

Types of data models

1. Conceptual Data Models

These models, which are often referred to as domain models, essentially provide a summary of the key business rules, organizational structure, and contents of the system. In order to aid in gathering requirements, conceptual models are produced early in the project. They pinpoint the essential elements that must be reflected in the system, along with their characteristics and interrelationships. These models, which are typically clear-cut and easy to use, also take security and data integrity into account.

2. Logical Data Models

Logical models are more detailed than conceptual models. They describe the data and relationships in the system more precisely, often using formal notation systems. Logical models also define data attributes, such as data types and sizes, but they don’t get into technical system details.

These models are sometimes skipped in agile or DevOps practices but are useful in projects that are data-focused, like designing data warehouses or reporting systems.

3. Physical Data Models

Physical models are the most detailed data models, they basically show how data will be stored in a database. These models provide a final design that can be implemented as a relational database. They include tables that represent relationships between entities, along with primary and foreign keys to maintain these relationships. Physical models also consider specific database management system (DBMS) features, such as performance tuning, managing, easy storing, and data retrieval.

Data Modeling Process

Data modeling is a method used to understand how data is processed and stored. It involves careful planning of how data will be represented and how it will meet

business needs. Different data modeling techniques use specific symbols and layouts to show how data is structured. Let us understand the basic steps involved in the data modeling process with the help of the steps written below

1. Identify Entities: The very first step of data modeling process is to start by identifying the key things, events, or concepts in the data set. These are called entities, and each one should be different from the others.
2. Identify Key Properties: As we have talked above that each entity in the data set must have unique characteristics known as attributes. Your main work in this step is to find those unique attributes for an entity. For example, a “customer” entity may have attributes like first name, last name, and phone number, while an “address” entity may include a street name, city, and zip code.
3. Identify Relationships: In the next step, determine how these entities relate to each other. For example, a customer may have a permanent address and an order may be shipped to a different address labeled as “office address”. These relationships are often shown using diagrams like Unified Modeling Language (UML).
4. Map Attributes: In this step, Your job is to make sure that all attributes are properly connected to their entities. This step ensures that the data model accurately represents how the business will use the data. Various patterns can be used for this purpose depending on the needs of the business.
5. Assign Keys and Normalize Data: In this step, you will assign unique identifiers, or keys, to link related data without duplicating information. This step, known as normalization will help in reducing the storage space but may affect how quickly the data can be accessed.
6. Finalize and Validate: Finally, In the last step, review and refine the data model to make sure it meets business needs. This process should be repeated as there might be data that needs to be changed over time.

This approach helps in creating a clear and organized structure for data, making it easier to manage and use.

Types Of Data Modeling

In today’s time, Data modeling has evolved a lot with database systems. With the passage of time, it has become more complex as data storage needs have grown. Here are some of the common types of data models that are widely used by companies to fulfill their needs and demands:

1. Hierarchical data models organize data in a tree-like structure, showing one-to-many relationships. Each record has a single root or parent that connects to one or more child tables. This model was first used in the IBM Information Management System (IMS) in 1966, especially in banking.While it is less efficient as compared to newer models, it is still used in systems like Extensible Markup Language (XML) and geographic information systems (GIS).

2. Relational data models were introduced by IBM researcher E.F. Codd in 1970 and are still widely used today. They organize data in tables where data segments are joined, making database management simpler. Relational databases often use structured query language (SQL) to manage data. These databases are effective at maintaining data accuracy and reducing redundancy, making them ideal for transaction processing system.

3. Entity-relationship (ER) data models basically use diagrams to show the relationships between entities in a database. Data architects use ER modeling tools to create visual maps that help in designing databases.

4. Object-oriented data models became popular in the mid-1990s with the rise of object-oriented programming. In these models, “objects” represent real-world entities which are grouped into class hierarchies with associated features. Object-oriented databases can handle complex data relationships and are used in multimedia, hypertext databases, and other advanced applications.

5. Dimensional data models were developed by Ralph Kimball to speed up data retrieval in data warehouses. Unlike relational and ER models, which focus on efficient storage, dimensional models prioritize quick access to information, even if it promotes redundancy. These models are commonly used in online analytical processing (OLAP) systems.

Advantages Of Data Modeling

Data modeling is a key part of building software or database systems. It offers several benefits and features which include:

1. Better Understanding of Data: Data modeling shows how data is organized and connected, which helps people in making better decisions about how to use and store it.
2. Better Data Quality: By finding mistakes and inconsistencies in data, data modeling improves the overall quality of the data and prevents issues.
3. Better Collaboration: It helps everyone involved in the development process to communicate more effectively, leading to better decisions and outcomes.
4. Increased Efficiency: Data modeling makes the development process smoother by providing a clear view of the data for software developers and other team members.

Data Modeling Tools

Today, many tools help with designing and managing computer systems, these data modeling tools are available in both ways- paid and free. Here are a few examples of data modeling tools that you can use during your development phase:

1. erwin Data Modeler: This tool helps in creating data models in an effective way. It uses a specific method called IDEF1X but also supports other crucial methods.
2. Enterprise Architect: This tool helps in visualizing and designing both enterprise systems and software. It follows object-oriented standards and is widely used across many industries.
3. ER/Studio: This is a database design tool that works with many popular databases. It supports in designing of both relational and dimensional data.
4. Open ModelSphere: This is a free, open-source tool for data modeling that is widely used by small businesses to manage their data effectively. If you are looking for a free tool to learn or understand data modeling process that this tool could be the best fit for you.