Data modeling is making a basic diagram of a software system and the data elements it covers, using text and symbols to represent data and how it flows. It offers a blueprint for designing a new database or redesigning an old application. Data modeling helps an organization use data effectively to meet business information needs.

A data model can be considered a flowchart showing the data entities, attributes, and relationships. It enables data management and analysis teams to document data requirements for applications and identify bugs in development strategies before any code is written.

Why is data modeling done?

Data modeling is an essential discipline in data management. A visual representation of data groups and their business context helps identify the information needs of different business processes. It then defines the properties of data items to be included in applications and the database or file system structures used to method, store, and manage data.

Data modeling can also help create common definitions and internal data standards concerning data management software. In addition, he plays a serious role in data architecture processes that document data origins, map how data travels through IT systems, and create a framework for managing data. Data modeling is also becoming vital for data scientists and analysts in developing more complex data science and advanced business intelligence and analytics applications.

Data Modeling Examples

The best way to picture a data model is to think about a building plan of an architect. An architectural building plan assists in putting up all subsequent conceptual models, and so does a data model.

These data modeling examples will clarify how data models and processes highlight essential data and how to arrange it.

1. ER (Entity-Relationship) Model

This model is based on the notion of real-world entities and relationships among them. It creates an entity set, relationship set, general attributes, and constraints.

An entity is a real-world object; for instance, an employee is an entity in an employee database. An attribute is a property with value, and entity sets share attributes of identical value. Finally, there is the relationship between entities.

2. Hierarchical Model

This data model arranges the data as a tree with one root to which other data is connected. The hierarchy begins with the root and extends like a tree. This model effectively explains several real-time relationships with a single one-to-many relationship between two different kinds of data.

For example, one supermarket can have different departments and many aisles. Thus, the ‘root’ node supermarket will have two ‘child’ nodes of (1) Pantry, (2) Packaged Food.

3. Network Model

This database model enables many-to-many relationships among the connected nodes. The data is arranged in a graph-like structure, and here ‘child’ nodes can have multiple ‘parent’ nodes. The parent nodes are known as owners, and the child nodes are called members.

4. Relational Model

This popular data model example arranges the data into tables. The tables have columns and rows, each cataloging an attribute present in the entity. It makes relationships between data points easy to identify.

For example, e-commerce websites can process purchases and track inventory using the relational model.

5. Object-Oriented Database Model

This data model defines a database as an object collection or recyclable software components with related methods and features.

For instance, architectural and engineering real-time systems used in 3D modeling use this data modeling process.

Types of Data Modeling

There are three main types of data models that organizations use. These are produced during the planning of a project in analytics. They range from abstract to discrete specifications, involve contributions from a subset of stakeholders, and serve different purposes.

1. Conceptual Model

It visually represents database concepts and their relationships, identifying the high-level user view of data. Rather than the details of the database itself, it focuses on establishing entities, characteristics of an entity, and relationships between them.

2. Logical Model

This model further defines the structure of the data entities and their relationships. Usually, a logical data model is used for a specific project since the purpose is to develop a technical map of rules and data structures.

3. Physical Model

This schema or framework defines how data is physically stored in a database. It is used for database-specific modeling where the columns include exact types and attributes. A physical model designs the internal schema. The purpose is the actual implementation of the database.

The logical vs. physical data model is characterize by the fact that the analytic model describes the data to a great extent. Still, it does not take part in implementing the database, which a physical model does. In other words, the logical data model is the basis for developing the physical model, which gives an abstraction of the database and helps to generate the schema.

Importance of Data Modeling

It is clear by now that data modeling is necessary foundational work. Allows data to be easily store in a database and positively impacts data analytics. It is critical for data management, data governance, and data intelligence.

• It means better documentation of data sources, higher quality, and clearer scope of data use with faster performance and few errors.
• It builds on business intelligence as it allows the identification of new opportunities by expanding data capability.

That was all about the article “What is Data Modelling.”

Frequently asked question

Q1. What is the data modeling process?

The first step in data modeling is identifying the use cases and logical data models. Then create a preliminary cost estimation. Identify the data access patterns and technical requirements. Create DynamoDB data model and queries. Validate the model and review the cost estimation.

Q2. How can AWS help with data modeling?

You can use Amazon RDS (relational database service) to implement relational data models, Amazon Neptune to implement graph data models, and AWS Amplify DataStore for faster and easier data modeling to build web and mobile applications.

Q3. What are data modeling concepts?

Data modeling concepts answer the question of WHAT the system contains. A conceptual model helps to organize, scope, and define business concepts and rules. These concepts are made by data architects and business stakeholders.

Q4. Why is data modeling important?

An organized and comprehensive data modeling is crucial to create a simplified, logical, and physical database. It is necessary to eliminate storage requirements and redundancy and enable efficient data retrieval.