%201.svg){kind=icon}
Confused by the term “data modeling”? Wondering how it can help your business and what is it all about? Look no further. So, to make it easier for everyone who wants to learn about data modeling,This blog explains all in the most simple and possible way after preparing a guide on everything you need to know about data modeling.
Introduction First, we discuss what data modeling is and why it is important. We walk through key data modeling concepts and principles. We then delve into data models — welcome to our whitepaper on the types of Data Models: conceptual, Logical and Physical & their examples.
At the end of this guide, you would have a clear understanding of data modeling and how to practically apply it in your business or projects. As such, we are going to unravel the enigma called Data Modeling and start you off on the path to unleashing your data.
Data — it's the essence of modern businesses. It informs decisions, spurs innovation and allows companies to gain a competitive advantage by providing valuable knowledge. But the problem occurs as data is increasing every day which are almost unmanageable. This is where Data modeling will comes in.
Data modeling is important. It has been leveraging data management and minimizing risks of data-related errors for organisations, ensuing the regulatory requirements, improved use of information across the entire organisation. In addition to this, a well-crafted data model can serve as effective destinations for data-driven decision-making and help businesses to make the best-informed decisions that drive business growth.
These concepts are the foundation of data modeling.
Entity: An atomic object or concept that exist in real world. Customers, Products, Orders and Employees as an example. An entity has a number of attributes(element) specific to it, i.e., customer entities would have certain parameters namely name, address or phone number.
Attributes: These are the properties that define an entity. It will store attributes of an entity such as the customer name, address and phone number.
Relationships: Logical associations among different entities, similar to one-to-one, one-to-many or many-to-many. These relationships define the linkage between entities in Data Model.
Data Type: Data types are used to define the type of data a field can be an example text, numbers, etc for coding inside the database program. Choosing the right data types lets your models optimize based on performance and prevents loss of precision.
Normalization: Normalization is the process of organizing data to reduce redundancy and improve data integrity. This means you would take your data and pre-aggregate it, breaking the data down into smaller pieces with a relation between them,k making this an efficient and scalable way to model your data.
Data modeling is multi-layered and used to capture different views with models in an organisation. There are three kinds of data models: Conceptual, Logical and Physical. All of us have their own purpose and plays important role in the Data Modeling process.
Conceptual data model: This is at the highest level and represents the wide concepts of an organisation's data. It defines the main entities and their attributes and relationships, without concerning details about how they will stored or implemented. Conceptual Model: A high-level view of data that is often used to communicate with business stakeholders and IT professionals.
In a conceptual data model for an e-commerce website, the key entities might include "Customer," "Product," and "Order," with attributes like "name," "email," and "address" for the Customer entity, and "product_name," "price," and "category" for the Product entity. The associations between these entities could be "a Customer can have many Orders" and "an Order can have multiple Products".
Conceptual data model will provide you more insights into your data, and how it is flowing across systems. This can provide a quick reality check on where potential data challenges or improvements may lie and also act as the starting point for more in-depth data modelling later on.
In conclusion, conceptual data modeling is an important part of the end to end data modeling process as it sets the stage for more detailed and implementation focused datamodels.
Once you have the conceptual data model, the next phase of data modeling process is to create logical data model. After a conceptual model is created, the logical data model takes it one step further, providing more technical details and refining the data structure to ensure that it can be put into database as well or other available storage system.
Elements such as data types, primary and foreign keys, and more detailed relationship definitions are included in the logical data model. It also takes into account aspects like data normalization, data integrity rules as well as other technical necessities that need to be followed in order to make the data model more sound and scalable.
For instance, a "Customer" entity in a logical data model for an e-commerce site may have attributes like "customer_id" (primary key), first_name," "last_name," "email," and phone_number. For instance, the "Product" Entity might have attributes like: product_id (Primary Key), product_name, description,price,category_id(foreign key) and inventory_count. The relations between these entities could be "a Customer can have many Orders", and "an Order contains many Products".
Creation of the Physical Data Model is the last Step in Data Modeling Process. Among these types, the most detailed and implementation-centric is of course the physical data model, which actually connects a high-level logical data model to an actual database or data storage technology.
A physical data model provides more specific details than a logical data model and includes use of structures such as the Table Definitions, Columns Names, Data types, Indexes etc to a point of detail that can only be defined once you will create the database. This also takes into account performance optimization, storage requirements and various other physical artifacts that are required for the data model to effectively take care of the organisations Data management and Analytics needs at hand.
An example of this -
This model defines the structure of a database for a hotel reservation system. It includes tables for hotels, rooms, and customers.
A hotel can have many rooms, and a customer can stay at multiple hotels. This model provides a blueprint for organizing and managing this data.
In data modeling, then tools such as ER diagrams and/or UML class diagrams are used to show entities, attributes and relations visually. When we inspect and profile data, techniques such as normalizing data are used in order to guarantee the data's integrity and quality. Professionals design models that are scalable and efficient and which match to the business using DBMS (SQL Server, Oracle) along with software like Trocoo for data modeling.Click here if you want to explore more about Trocco - a modern platform to manage your data integration and modeling needs. Trocco offers a comprehensive solution to streamline your data processes and maximize your business insights. Get a free trial today!
%201.svg){kind=icon}
