Over the years, many businesses have been cautious about decision making processes that affect them. This is important since decisions made by a business determines its success. The part of the decision is forecasting which can be made possible by studying the growth of the business. The oil for decision making has been data. It is through it where one can obtain insights about the business and the growth pattern. Decision making process is greatly characterized by the data modelling process.
Data Modelling refers to the process of analyzing and define all the data types your business collects and produce,as well as the relationship between those bits of data. This can be achieved by using different tools in the tech field. Modelling can be represented using text, symbols and diagrams since it represents how the data is captured, stored and used.
Data Modeling Process
This refer to the process of creating conceptual representation of objects and their relationship to one another.The process of data modeling typically involves specific defined steps which are;
Requirements gathering.
Conceptual design.
Logic design.
Implementation.
During each step the data modelers work with stakeholders to understand the data requirements, define the entities and attributes, establish the relationships between the data objects, and create a model that accurately represents the data in a way that can be used by the stakeholders.
Levels of abstraction.
Conceptual model- collaborate with stakeholders to understand the data requirements, identify the entities and attributes, build the links between the data objects, and develop a model that accurately represents the data in a usable format.
logical level-The logical level involves defining the relationships and constraints between the data objects in more detail, often using data modeling languages such as SQL or ER diagrams.
Physical level-The physical level involves defining the specific details of how the data will be stored, including data types, indexes, and other technical details.
Data Modeling Examples
The best way to picture a data model is to think about a building plan of an architect. An architectural building plan assists in putting up all subsequent conceptual models, and so does a data model.
Entity Relationship Model:This model is based on the notion of real-world entities and relationships among them. It creates an entity set, relationship set, general attributes, and constraints.
Hierarchical Model:This data model arranges the data in the form of a tree with one root, to which other data is connected. The hierarchy begins with the root and extends like a tree. This model effectively explains several real-time relationships with a single one-to-many relationship between two different kinds of data.
Network Model:This database model enables many-to-many relationships among the connected nodes. The data is arranged in a graph-like structure, and here ‘child’ nodes can have multiple ‘parent’ nodes. The parent nodes are known as owners, and the child nodes are called members.
Relational Model:This popular data model example arranges the data into tables. The tables have columns and rows, each cataloging an attribute present in the entity. It makes relationships between data points easy to identify.
Object-Relational Mode:This model is a hybrid of an object-oriented database and a relational database. As a result, it combines the extensive functionality of the object-oriented paradigm with the simplicity of the relational data model.
Benefits Of Data Modeling.
Allows the developers and the stakeholders to understand the relationship between different objects for easier analysis.
Improved data quality: Data modeling can help to identify errors and inconsistencies in the data, which can improve the overall quality of the data and prevent problems later on.
Improved collaboration: Data modeling helps to facilitate communication and collaboration among stakeholders, which can lead to more effective decision-making and better outcomes.
Increased efficiency: Data modeling can help to streamline the development process by providing a clear and consistent representation of the data that can be used by developers, database administrators, and other stakeholders.
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