Creating a Data Model with ChatGPT is Easier than you think

In today's data-driven world, the ability to build accurate and efficient data models is paramount for businesses and individuals alike. However, the process of constructing a data model can often be complex and daunting, requiring specialized knowledge and technical skills. But what if there was a way to simplify this process and make it accessible to a wider audience? Enter ChatGPT, a powerful language model developed by OpenAI. In this article, we will explore how ChatGPT can be leveraged to build data models easily, using a practical example. By harnessing the capabilities of ChatGPT, you'll discover how data modeling can become a more approachable and intuitive task for everyone, regardless of their technical background.

Build Data Model with ChatGPT

Consider data modeling as the process of drawing diagrams for software applications that provide an overview of all the data pieces they include. The data flow is depicted in the diagram using text and symbols. It serves as a model for creating a new database that will allow a company to utilize the data efficiently for its needs. The primary objective of the data model is to establish an overall picture of the types of data that are used, how they are kept in the system, the relationships between the data entities, and the various ways in which they can be arranged and grouped. The norms and procedures for gathering feedback from the business stakeholders are taken into consideration when building data models.

The Data Model functions as a better understanding of what is designed, much like a roadmap or blueprint might. It offers a comprehensive review of the standardized methodologies and schema to define and manage data in a way that is common and uniform throughout the organization. According to the level of abstraction, there are three different types of data models.

• Conceptual Data Model: It provides a helicopter view of the system description, its organization, and business rules to be considered. Initial project requirements are captured using the conceptual model. It mainly consists of the business entities, their constraints and characteristics, and the relationship between them for data integrity and security requirements.
• Logical Data Model: The logical data model provides detailed insights into the concepts and relationship which consists of data attributes and the relationship between the entities. It is very much useful for data-driven projects or initiatives.
• Physical Data Model: It provides an overview of how the data should be stored physically within the database. It is a final design to implement the relational database including the relationship using the primary and foreign keys.

                 Image 1 : Types of Data Modelling Techniques

The data model was created using a variety of data modeling methodologies, as seen in the graphic above. The most popular data modeling technique utilized by any corporate organization is entity relationship modeling, also known as dimensional modeling. Erwin Data Modeler, ER/Studio, Archi, and other tools are available on the market to construct data models utilizing these data modeling methodologies. The data Modelling technique mainly involves below steps :  

• Identify the entities
• Find the entity properties
• Create a relationship between the entities
• Correlated attributes to the entity
• Define the degree of normalization to improve the performance
• Validate and Finalise the data model

Let’s start with creating a data model using chatGPT. The goal is to ask chatGPT to start with the data modeling activities for the anti-money laundering(AML) system of a banking domain:

Image 1: The data model for the banking system, Part 1
 

 Image 2: Data Modelling  for AML Process for Bank

 As you can see in the image, once we provide an input to the chatGPT, it provides a step-by-step process of building the data model. The first step is to understand the AML regulations and identify the stakeholders for the system to capture the requirements. Once the stakeholders are identified, the next step is to define the data modeling goals including the list of data sources, and perform the data profiling. Once data profiling steps are done, the next activity is to create a conceptual, logical, and physical data model.

Now, Let’s check with chatGPT to create a conceptual model with all the entities.

Image 3: Conceptual Data Model, Part 1

                 Image 4 : AML Conceptual Model

After the input, chatGPT responds with the list of actors, entities, and relationships between the entities to define the conceptual model. With this information, we can have a high-level overview of the system by building the conceptual data model. 

Let’s ask chatGPT to build the logical data model once the conceptual data model is ready:

Image 5: AML data model for ERwin Tool

 

                 Image 6 : AML Logical Data Model, Part 2

 As you can see in the above image, step by step process to create a logical data model is to open the Erwin Tool and create a new data model. In the new data model, add all entities, their attributes, and the relationship between the entities. Once entities are defined, set up primary and foreign keys for all entities and validate the data model. After the validation, adjust the review comments and finalize the logical data model and generate the documentation for the same.

Next, Let’s ask chatGPT if it can add new customer information to the existing conceptual model.
 

                 Image 5 : AML Logical Data Model with Customer Information

 As we can see in the above image, chatGPT asks to first identify the source information and create an entity and attributes for the same. Once it is done, we have to define the cardinality to understand how entities are related to each other. Then define primary and foreign key relationships, data model validation and generate documentation.

Conclusion

In this article, we understood the importance of building the data model and step by step process to create the data model. Later in this article, we also checked how to use chatGPT to create conceptual and logical data models.

Author Bio

Sagar Lad is a Cloud Data Solution Architect with a leading organisation and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.

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