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A Comprehensive Guide to Understanding the Stages & Types of Data Models 101


By LekhanaPublished 2 months ago 6 min read

When mapping out database linkages and activities, data modeling is arranging and cleaning data into a visual representation. Regardless of the contents of the database, many kinds of data models can be used as a guide to building a successful one.

The process is finished by a data modeler, who works directly with data entities and attributes to ascertain their links and create an appropriate model. Additionally, data architects work on different data models, concentrating on developing structural blueprints.

This blog discusses the various types of data models, the data modeling method, and a few data modeling tools.

What is Data Modelling?

A data model is a visual method of building a database that enables you to start with broad concepts. It offers a consistent framework for representing actual entities and their properties. These entities are typically connected in some way and have been broken down into their most fundamental components. Everything, including commodities, their qualities, and interactions between objects, is stated in a pretty simple way. You can describe how entity data is organized, stored, and searched with the help of data models.

The Data Model previews the system's final appearance after it has been fully deployed. It describes the data elements as well as their connections. Data models are used in database management systems to display how data is stored, connected, accessed, and modified. For the organization's members to communicate and understand the information, we present it using a set of symbols and language. For further information on data modeling techniques, refer to the best data science course available online.

Different types of Data Models in Data Science

Many stakeholders are involved while dealing with the various sorts of Data models. There are three different types of data models; as a result, one for each stakeholder. The subsequent are:

Like any other design process, database and information system design begins at a high degree of abstraction and eventually becomes more concrete and specific. There are three different data models, depending on the level of abstraction they provide. A conceptual model will serve as the starting point of the strategy, which will then go on to a logical model and, finally, a physical model. Each of the several sorts of Data models is covered in further detail in the following parts.

Conceptual data model

The first type of data model is the conceptual data model. They are also referred to as domain models because they offer a broad overview of the system's contents, structure, and business rules that will be applied. Conceptual Models are typically produced as part of the initial project requirements-gathering procedure. Entity classes, their constraints, relationships, and any applicable security and Data Integrity standards are often included. Entity classes specify the things essential for the firm to represent in the Data model. Any notation is typically simple to understand.

Logical data model

The second form of the data model is the logical data model. They offer additional details on the concepts and connections in the topic and are less abstract. It uses one of the official notation schemes for data modeling. The relationships between entities are displayed in logical data models, together with data attributes like data types and lengths. Logical data models do not provide the technical system requirements. The logical data Modeling stage is usually ignored in agile or DevOps methodologies. Logical data models may be helpful in highly procedural implementation contexts or for projects that are data-oriented by nature, such as data warehouse design or reporting system development.

Physical data model

The final type of data model is the physical data model. They specify the structure in which data will be kept in a database on a physical level. They are the least abstract of the group as a result. With associative tables that display relationships between entities, Physical Data Models offer a finalized architecture that may be used as a Relational Database. In order to maintain those relationships, both primary and foreign keys will be used. The characteristics of a Database Management System (DBMS), such as performance tuning, may be included in Physical Data Models.

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Data Modeling Methodology

As a field, data modeling encourages participants to evaluate data processing and storage thoroughly. Different conventions control how business demands are conveyed, how models are constructed, and which symbols represent data in data modeling approaches. All methods offer structured workflows, which are lists of tasks that must be finished one after the other in a particular order.

An overview of a workflow is shown below:

Pick out the entities – The first stage in the data modeling process is to identify the things, occasions, or concepts represented in the data collection to be modeled. Each entity must be logically distinct from the others while also feeling cohesive.

Identify the crucial characteristics of each entity – Each entity type is distinguished by one or more distinctive features or attributes. On the other hand, an "address" entity might have a street name and number as well as a city, state, nation, and zip code, whereas a "customer" object might only have a first name, last name, phone number, and salutation.

Establish the connections between the entities – The nature of each entity's relationships with the other entities will be specified in the first draft of a data model. Each client in the above case "resides" at a specific address. Each order would be shipped to and paid for at a particular address if the model expanded to include an object called "orders." Frequently, these linkages are described by an individual modeling language (UML).

Finish mapping attributes to entities – This guarantees that the model accurately reflects the company's data use intentions. Different formal Data modeling paradigms are in use. Although stakeholders from other business areas may use various patterns, object-oriented developers frequently use analysis and design patterns.

Choose a level of normalization that strikes a balance between the need to reduce redundancy and the requirement for performance. Assign keys as necessary. Normalizing data models (and the databases they represent) involves assigning numerical identifiers, or "keys," to groups of data to show relationships without repeating the data itself. Suppose every customer is given a key, for instance. In that case, it will be possible to link that key to their address and order history without duplicating the data in the customer database. Normalization decreases a database's need for storage space while sacrificing query performance.

Complete and test the various Data model kinds – Data modeling is an iterative process that should be repeated and updated as business demands change.

Advantages of data modeling in Data Science

Data modeling allows developers, data architects, business analysts, and stakeholders to look at and understand relationships between data in a database or data warehouse. Additionally, it has the capacity to:

Minimize errors during database and software development.

Boost system architecture and documentation uniformity across the whole organization.

  • Improve the functionality of your database and application.
  • Organize data mapping more efficiently.
  • Boost communication between the business intelligence and development teams.
  • Simplify and accelerate database design at the conceptual, logical, and physical levels.


Making data models will be helpful for software developers, data scientists, and data architects. Knowing when to use the right data model and how to involve business stakeholders in the decision-making can be quite beneficial. I hope you learned about the many kinds of data models and the data modeling method. If you are keen to learn more about data modeling and other data science techniques, feel free to check out the best data science courses in India, having the best industry-relevant curriculum and are taught by experienced trainers.


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