Conceptual Database Modeling 101: A Complete Guide, Simplified

Simply put, Data Modeling is the act of developing/brainstorming visual representations of data to be kept in databases. You can maintain consistency while labeling data sets, their attributes and semantics by utilizing data models — implying that data modeling can guarantee a certain level of data quality. 

Modeling data can be done on three levels: conceptual, logical, and physical. A Conceptual Data Model is a high-level view of data used to identify items and their connections. As a result, a conceptual data model defines what is in and what is out of the database in a highly abstract manner. Hence, a conceptual data model’s goal is to define, scope, and arrange various data elements and their interactions without focusing on their characteristics.

In this blog post, we’ll be discussing Database Modeling and its stages. Later in the post, we’ll deep dive into the how and why of starting to build a Conceptual Database Model. This blog post also has a BONUS section for those users interested in creating a new project from scratch and who want to know which all tools they can leverage. So, let’s begin.

What is Database Modeling?

Data modeling visualizes information systems or some sections of it to communicate linkages between data points and their structure. The goal is to demonstrate the many formats of data utilized and stored inside the system, relationships between different data types, and the various ways the data may be categorized and arranged.

3 Stages of Database Modeling

Conceptual Data Model: The Conceptual Data Model identifies what the system contains. Business stakeholders and data architects are usually the ones who build this model. In short, the goal is to collect, define, and scoop out new business concepts and standards.

Logical Data Model: Regardless of the DBMS, the logical data model defines how the system should be implemented. Data Architects and Business Analysts are usually the ones who build this model. The goal is to create a technical map of data structures and rules.

Physical Data Model: This Data Model defines how the system will be implemented using a certain DBMS system. DBAs and developers are usually the ones who construct this model. The goal is to get the database up and running.

Let’s Talk About Conceptual Database Modeling — in detail!

The conceptual data model allows the designer to get a big picture of the system they’re working on without having to worry about the minutiae of implementation. This is the moment to ask questions, interact with stakeholders, and think about the business requirements and processes that the database will address. Although conceptual data models are quick to construct, they can also quickly reveal incorrect assumptions and potential issues. The conceptual model is a simplified representation of the final database, with the specifics removed to focus on the broad picture.

Why is Conceptual Database Modeling Important?

By skipping the conceptual data modeling stage, big-picture entity linkages like the customer/prospect distinction are more likely to be overlooked. Cardinality is also taken into account in conceptual data models. Cardinality specifies how an entity interacts with other entities, including one-to-one, one-to-many, and many-to-many interactions.

Such an omission becomes increasingly frequent without conceptual data modeling as an organization progresses through the development cycle. This is because when teams are focused on design minutiae and under time constraints, the big vision is typically forgotten. Organizations may avoid such oversights and see that ambiguous wording, so the possibility for relationships between things is evaluated and established ahead of time, by first developing the conceptual database model.

Key features of Conceptual Database Modeling

• Define Business Needs in Crisp & Precise Fashion: From a resource and time management standpoint, a conceptual model can help the key stakeholders better grasp what is required to achieve their intended business goals.
• Give Organization a Business Vocabulary: By presenting a picture of concepts, entities, and their relationships, gaps in language can be identified and oversights eliminated.
• Foster Analysis-driven Form of Communication: Stakeholder input from both inside and outside the tech bubble is required to ensure successful adoption. Conceptual data models aid collaboration.
• Build Business Plan For Favorable Outcomes: Conceptual data modeling can be thought of as the first step toward more complex data modeling. Modelers can begin to add context to the model once the conceptual model is complete. That would be the database’s data requirements in a logical data model. The context for physical data modeling now includes the database management system (DBMS) being modeled for.

What Purpose Does Conceptual Database Models Solve?

A conceptual data model should be used to establish and express high-level relationships between concepts/entities. To put it another way, they assist an organization in seeing its data — and the links between different forms of data — in context.

They should ideally be visual representations of data in a context that tell the story of how a company performs in specific situations. This can assist firms in avoiding oversights that could result in major issues down the road. When developing or purchasing a new customer relationship management (CRM) system, for example, the requirement to differentiate between a prospect and a customer may not be obvious. A “new” prospect could be an employee of a company with an existing account if that difference is not made.

A database that distinguishes between prospects and customers can also be modeled to recognise any potential relationship between a new prospect and an existing customer, allowing the record to be consolidated. This provides the background that sales and support reps require to function effectively.

But, How a Competent Conceptual Database Model Work & Look? 

In general, to create a Conceptual Database Model, an ER Model works the best. On the other hand, we can easily define entity relationships by using Crow’s Foot or Chen notation. In this blog post section, we will unwind the how behind making a competent Conceptual Database Model. 

Library Database Conceptual Database Model

Let’s draw up the data items and relationships that a library database requires. Each physical book can be paired with only one library, and the library can include zero or many books. The relationship between the library and its members is the same, thus we can use the same Crow’s Foot notation.

When we introduce the concept of lending books to members, we see a new relationship emerge. While a member can have zero or many loans, and a loan can only be associated with one member, each book can have zero or one loan, and a loan can only be associated with one book.

This simplistic model does not reveal much about how the library will function, but it does prompt us to consider the entities that will need to be represented in the final library database.

Order Management Conceptual Database Management System

Now, let’s think how an order management conceptual database management system. This CDM can be used by retail or wholesale stores.The crucial thing is that it simulates the data objects that will need to be tracked as well as their probable relationships. Let’s start with the client. Each client can send zero or many orders, but each order can only have one customer.

The order may contain one or more items, but each item can only be associated with one order. Each of the items is linked to a real-world product. Each order item can have just one item, however, products can have zero or many order items linked with it. The products could be in the shop database without ever being used in a customer order.

The consumer will have to pay for the order at some point, thus an invoice will be required. The process will be completed when an invoice is generated from the products listed in the order. Each item can be associated with only one invoice, and the invoice can contain one or more items.

BONUS: 5 Best Data Modeling Tools

Draw.io

Draw.io is compatible with all browsers, including Firefox and Chrome, and is mobile-friendly. Few free tools provide as much freedom as this one. It has no size restrictions. The program also allows users to choose where they want to save the model. It also includes a ‘offline’ mode. Choose from a variety of pre-made templates and utilize the drag-and-drop tool to get speedy results. It allows users to generate various diagrams, such as ERD and UML.

SAP PowerDesigner

PowerDesigner excels at handling complex data. Its free edition has few limits but is quite user-friendly. Metadata management and link-and-sync technologies are two of the best features. The mapping editor works quickly. Additionally, it can perform impact analysis and manage multi-model documents. The software supports three file extensions (.pdm,.cdm, and.bpm), as well as web-based reporting.

Lucidchart

Lucidchart is the Lucid visual collaboration suite’s intelligent diagramming application—the it’s quickest and easiest method for individuals and teams to map out their organization’s processes, systems, and people. You can work from anywhere, at any time, on any device or browser because it’s cloud-based. As you conceptualize complex ideas and bring them to life, this makes it simple to collaborate and align with teammates. Empower your teams to decipher ambiguity, unify their insights, and build the future more quickly.

Visual Paradigm

You can export the database from the Entity-Relationship Diagram using this tool (ERD). For design purposes, new users can rely on the REST API. If you get stuck, you may rely on the smart wizard, which walks you through the process of creating a database step by step. The collaboration function of this data modeling tool is one of its features, since it allows team members to collaborate on a project at the same time. You will be able to create reports using Visual Paradigm. It also works on practically all operating systems, including Linux, macOS, and Windows, unlike some other programs.

isee systems

This useful tool uses charts, graphs, and animations to assist students in discovering links between a range of variables. It’s enjoyable to use because of the drag-and-drop feature. Sensitivity, simulation, and time-series models are all supported by this tool. Even though it is a premium product, you can acquire a free trial to learn more about how it works.

Conclusion

Through this blog post, we got an overview of how Conceptual Data Modeling works. We also got through two examples where we explained how a simple Conceptual Database Modeling is done. With the help of this conceptual information, you can progress through the in-roads of Data Modeling to build something that is great, easy, and elegant.

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