Data Modeling Definition | What is Data Modeling

In this article, you’ll learn what data modeling is, different types, key benefits of using data modeling, techniques, and real-world examples.

What is Data Modeling? 

Data modeling means creating a visual representation of a software system and its components in diagrams and flowchart that help users to easily understand the process/system by using text and symbols. It helps learners understand the key points and data relationships without making errors and use it according to their needs. 

Types of Data Modeling

There are many types of data modeling available but mainly three types of data modeling preferred: 

1. Conceptual Data Models

They give you a bird-eye view of the system and the data attributes and its relationships. The organizing of the system, its data flow and parts of concentration in a specific field are also certified. The structure and the flow come as the first step. And here, the parent and child tables or the one-to-one link-ups are shown.

Here, the company sees the format they will have in their business for representing and storing data. And since data modeling has enormous importance in the market and with their other partners to discuss and interpret, they might also want to include data security and integrity.

2. Logical Data Models:

After the conceptual data modeling, we have the logical data models fill the gaps in that it provides the data types and the relationships. The presentation shows better details, though they do not go for the technical ends.

Logical modeling can save up when it comes to knowing the data types and the way the organizing can be conducted. Like conceptual models show the flow, logical models add in the entities.

3. Physical Data Models

Physical data models give you the result in a sense that they have the data input, correct flow, relationships established, and all of these formally organized for the business.

These can even be turned into relational databases, with primary keys to identify the data without redundancy and foreign keys to link up the data and tables. 

📌 Further reading

Key Benefits of Data Modeling

Data modeling has been proven effective in understanding and interpreting data in formal sectors. It has provided a simple way of showcasing the complex data structure and flow. It has also come with its unique benefits, with some listed below.

  • Boosts database and data warehouse performance. 
  • Increases the understanding of the data for the team.
  • Improves the data interpretation and overall communication in the organization.
  • Reduces the chance of errors in the databases.
  • Adding to the previously mentioned point, it also adds to the speed and effectiveness of the database and, in return, the whole business.
  • Improves consistency and reduces duplication,
  • Improved quality of representation with easier understanding and easy-to-understand laid-out structures.

Data Modeling Techniques

In the data modeling sector, there are many ways to construct and work upon a given set of data. These types often determine the relationship between the data and how to study them. And according to the accessibility, all the types have different bases of primary usage.

For instance, some in finance, some in technology and agile software development. Here are a few of the modeling types popularly used today: 

1. Hierarchical Models

This type was introduced by IBM to the world in 1968 and has been used very popularly, and the banking sector is its primary user. It has a relationship that goes from a parent to further child tables, spreading out in a treelike structure. However, more recent developments have overstepped their benefits so far.

2. Relational Data Models

Used mainly in relational databases, these models emphasize simplicity in the context and format. The data models in this type are built by SQL (structured query language) and have the tables in the database linked up. These can then be followed through a fundamental grasp of the databases. It was mainly implemented to save time and employ a more streamlined framework.

3. Entity-relationship data models: 

Visual entities perform as the core here, with the diagrams and other formats used to showcase the data relationships. Different tech teams use ER modeling tools extensively to represent the data through simple and conveyable visual metrics. 

Examples of Data Modeling

Data modeling, as we discussed, can come off in different types, each with its implementation. We also have conceptual data modeling and logical and physical models. However, not everyone needs the three of them in the real world. 

1. Conceptual data modeling

Examples of conceptual data modeling include employee or student management models, even for corporations or universities. They have divisions where they can be assigned to a single subject (as in a department) or any project.

These models serve the best by providing the primary accounts of division numbers and the task assigned to each group. The online shopping apps also have a similar structure followed up with the customer details to the Wishlist/cart and then the product they choose to purchase from the site. Reservation systems work similarly as well. 

2. Logical data modeling

In logical models, you might want to have a requirement of details that can and need to be connected to the primary key or the primary data. Such as, you need customer details, but they can also vary over time, so a logical model is a safe choice. 

3. Physical data modeling

On the other hand, physical models can be used for calendars, notes or diaries, etc., as these have the most structured foundations available.

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