What is Data Modelling?
Data Modelling is the process of analyzing the data objects and their relationship to the other objects. It is used to analyze the data requirements that are required for the business processes. The data models are created for the data to be stored in a database. The Data Model’s main focus is on what data is needed and how we have to organize data rather than what operations we have to perform.
Metadata and data modeling tools support the creation and documentation of models describing the structures, flows, mappings and transformations, relationships, and quality of data.
Data Model is basically an architect’s building plan. It is a process of documenting complex software system design as in a diagram that can be easily understood. The diagram will be created using text and symbols to represent how the data will flow. It is also known as the blueprint for constructing new software or re-engineering any application.
Uses of Data Modelling Tools
Data Modelling is a process to formulate data in an information system in a structured format. Listed below are certain practical uses of the related tools in any sector or industry.
- Data Modelling helps create a robust design with a data model that can show an organization’s entire data on the same platform.
- The data model makes sure that all the data objects required by the database are represented or not.
- The database at the logical, physical, and conceptual levels can be designed with the help data model.
- The relation tables, foreign keys, and primary keys can be defined with the data model’s help.
- Data Modelling Tools help in the improvement of data quality.
- Data Model gives the clear picture of business requirements.
- Redundant data and missing data can be identified with the help of data models.
- In data models, all the important data is accurately represented. The chances of incorrect results and faulty reports decreased as the data model reduces data omission.
- The data models create a visual representation of the data. With the help of it, the data analysis gets improved. We get the data picture, which can then be used by developers to create a physical database.
- Better consistency can be qualified with the help of a data model across all the projects.
- The data model is quite a time consuming, but it makes the maintenance cheaper and faster.
Three Perspectives of a Data Model
Data Modelling helps to create a conceptual model and create the relationship between the items. The basic data modelling techniques involve dealing with three perspectives of a data model.
1. Conceptual Model
The conceptual data model is a view of the data that is required to help business processes. It also keeps track of business events and keeps related performance measures. The conceptual model defines what the system contains.
This type of Data Modelling focuses on finding the data used in a business rather than processing flow. The main purpose of this data model is to organize, define business rules and concepts. For example, it helps business people to view any data like market data, customer data, and purchase data.
2. Logical Model
In the logical data model, the map of rules and data structures includes the data required, such as tables, columns, etc. Data architects and Business Analysts create the Logical Model. We can use the logical model to transform it into a database.
This type of Data Modelling is always present in the root package object. This data model helps to form the base for the physical model. In this model, there is no secondary or primary key is defined.
3. Physical Data Model
In a physical data model, the implementation is described using a specific database system. It defines all the components and services that are required to build a database. It is created by using the database language and queries. The physical data model represents each table, column, constraints like primary key, foreign key, NOT NULL, etc.
The main work of the physical data model is to create a database. This model is created by the Database Administrator (DBA) and developers. This type of Data Modelling gives us the abstraction of the databases and helps to create the schema. This model describes the particular implementation of the data model. The physical data model helps us to have database column keys, constraints, and RDBMS features.
Data Modelling Techniques
Below given are 5 different types of techniques used to organize the data:
1. Hierarchical Technique
The hierarchical model is a tree-like structure. There is one root node, or we can say one parent node and the other child nodes are sorted in a particular order. But, the hierarchical model is very rarely used now. This model can be used for real-world model relationships.
2. Object-oriented Model
The object-oriented approach is the creation of objects that contains stored values. The object-oriented model communicates while supporting data abstraction, inheritance, and encapsulation.
3. Network Technique
The network model provides us with a flexible way of representing objects and relationships between these entities. It has a feature known as a schema representing the data in the form of a graph. An object is represented inside a node and the relation between them as an edge, enabling them to maintain multiple parent and child records in a generalized manner.
4. Entity-relationship Model
ER model (Entity-relationship model) is a high-level relational model which is used to define data elements and relationship for the entities in a system. This conceptual design provides a better view of the data that helps us easy to understand. In this model, the entire database is represented in a diagram called an entity-relationship diagram, consisting of Entities, Attributes, and Relationships.
5. Relational Technique
Relational is used to describe the different relationships between the entities. And there are different sets of relations between the entities such as one to one, one to many, many to one, and many to many.
In a nutshell, Data Modelling helps in the visual representation of data. Data Models are built during the design and analysis phase of a project to ensure those application requirements are fulfilled. This is what Data Modelling keeps on the table for us.