A data model is the most important element of any operating system. It involves a visual representation of data structures and relationships, which is helpful in organizing and optimizing data for efficient access, analysis and management. In this guide, you will learn various techniques for data modelling, their applications, and the best practices that will help you build strong and reliable data architectures.
First of all, you need to understand what exactly is a data model?
What is a Data Model?
A data model is a configuration of data structures and business rules. It creates a visual representation of data and illustrates how various data elements are related to each other. It will also answer the who, what, where, and why of the elements in the data. Now, let’s take an example of a retail transaction in which the data gives the details about the customers, the things they brought and the time of the purchase. This model will also have information about the product, store, salesperson, manufacturer, supply chain, etc.
Why is Data Modelling Important?
Data modelling is important for the following reasons:
- Consistency in Data: It maintains uniformity across data sources.
- Quality of data: It ensures the accuracy and integrity of the data.
- Efficiency: Data models optimize the performance of a database.
- Scalability: A data model enhances the scalability of a system to handle data numbers that are growing over time.
- Communication: A data model is essential in improving communication between stakeholders by providing a clear data structure.
Types of Data Models
There are three types of data models: conceptual, logical, and physical data model.
- Conceptual Data Model:
- It provides a high-level overview of the data of your organization.
- It effectively defines entities, their characteristics and relationships without exploring their technical details.
- This data model is used by business stakeholders to understand the landscape of their data.
- The best example of a conceptual model is a detailed entry-relationship diagram that illustrates customer data, information of the order and the product details.
- Logical Data Model:
- It offers a comprehensively detailed representation of data structures and relationships.
- It focuses on the attributes, type of data and their relationship in a precise manner.
- This data model is mainly used by various data architects and analysts to create a database design.
- The best example of the logical data model is a detailed ERD that showcases data attributes, along with primary and foreign keys.
- Physical Data Model:
- This model translates the logical data into a physical schema that will be used in the system.
- It defines the actual tables, columns, indexes, and constraints of the data.
- This model is used by database administrators for data implementation.
- SQL scripts are the best examples in this data model. They are used for creating relationships and tables of the database.
Techniques of Data Modelling
It is always important to understand how data modelling has evolved with time to understand the popular techniques that are used today. So, here are some of the techniques, including those which were used in the early days of databases and the ones that are used currently:
- Hierarchical Data Model:
In this model, the user stores the data in a tree-like structure, just like a parent and child record that involves a collection of data fields. In this model, a parent can have more children, but the child will only have one parent. This model also comprises links that act as a connection between the records, along with the types that specify the kind of data contained in the field. It was originally used in the mainframe databases during the 1960s.
- Network Data Model:
This model is the extended version of the hierarchical model. Which allows the child to have one or more parents. Its standard specification was globally adopted in 1969 by the Conference of Data Systems Language, which is a now-defunct group, due to which this model is also called the CODASYL model. This technique is the forerunner to the graph data structure, which also has a data object represented inside a node and the relationship between two nodes, also known as edge. It is still popular on mainframes despite being replaced in the late 1970s.
- Relational Data Model:
In this model, the data is stored in tables to get the columns and the relationship between the data elements recognized. It also incorporates uses features from the database, such as constraints and triggers. This technique became dominant in the early 1980s and is an inspiration of the entity-relationship and dimensional data models that can also be used with non-relational databases.
- Object-Oriented Data Model:
This technique is a unique blend of object-oriented programming and relational data models. In this model, an object represents data and its relationships in a single structure, along with its attributes that specify the properties and methods of the object, apart from the methods that define this behaviour. This model was created to be used with the object databases, which came into fray in the late 80s and early 90’s as an alternative to relational database software. This technique is still used today for developing various applications.
Some other data techniques that are used in modern-day are:
- Entry-Relationship Modelling
- Schema-less(NoSQL) Techniques.
- Data Vault Modelling
- Data Mesh
- Graph Data Modelling
- Time Series Data Modelling
Best Practices To Use in Data Modelling
You need to follow some of the practices that can be useful in modern-day data modelling given below:
- Understanding Business Requirements.
- Simplification and Normalization
- Usage of modern appropriate techniques
- Ensuring scalability
- Focus on Data Quality.
- Document your findings.
- Focus on optimizing the performance of the data tables.
Final Words
Data modelling is the most important aspect you need to know if you want to design effective data. You can create a robust, efficient, and scalable data structure by understanding these data modelling techniques. Whether you want to build a transactional system, a data warehouse, or a NoSQL database, the suitable data model can effectively enhance data management, access, and analysis to ensure your data structure is supporting the goals of your business.