Data Modelling Definition
Data modeling can be defined as the activity of associating an entity of a real-world business with an object of a system or a database. It then becomes the map for creating and establishing the databases in a manner that a data architect or any stakeholder can comprehend the creation and design of the databases within the dataset.
Data models are also developed through diagrams that are standardized in terms of the symbols and notations used to relay how data elements are related and hence make the work of designing and altering database systems easier. It acknowledges numerous difficulties and suboptimalities in the organization of the material prior to its implementation, thus decreasing the prospects of costly mistakes and futile efforts.
With this, the framework offered assists in a straightforward solution of handling data and makes communication between the team members and other important stakeholders better, aids in arriving at improved decisions and creates a basis for systems that are more sustainable at a higher level.
Types of data modelling
It is possible to distinguish several types of data modeling that are oriented on different levels of goal and abstraction in the life cycle of data.
Conceptual type. Conceptual data modeling only shows the structure of data; in this way, it only shows the main entities and the relationships between them with basic attributes.
The entity relationship model is used right from the initial stage of a project where a global vision is made and the goals are set. Such a model is applied in sharing of information between various stakeholders with the purpose of reaching a consensus as to the features of the data setting.
Logical type. Logical data modeling specifies what attributes the entity consists of and how the entities are related to each other, so, consequently, it allows for defining more characteristics than CDM.
Independent of any particular platform, it deals with data requirements and governing rules of data types and constraints. Logical models enable the establishment of a general database plan that can be applied to any DBMS once it has been set up.
Physical type. Physical data modeling is the process of developing a logical model into a specific database schema, which details the actual implementation. Details include tables, columns, data types, indexes, and relationships. The physical model deals with performance, storage, and retrieval mechanisms in optimizing the database for technical requirements.
Other types. Certain variations of methods in data modeling are used to serve the given specific needs of dimensional data modeling for data warehousing and business intelligence.
Hierarchical and network data models convert the data into forms of trees and other graphical forms suited for the legacy applications relevant in this field. These are specialized models that handle other tasks apart from just designing a database such as an everyday usage database.
The importance and benefits of data modeling
Data modeling has played a major role in linking business requirements that were often unmanageably complex. The business side must be able to communicate effectively with the technical experts so that they can work together to create a database that enables the company to use its data efficiently within the workflow.
- Linking business and technology
Through the mapping of entities to objects in the real world, data modeling offers a universal language by which the stakeholders can communicate, thus ensuring that the projects are not derailed due to a lack of understanding.
- Scheme for correctness
Among the major benefits of data modeling should be mentioned that data modeling provides a comprehensive blueprint according to which the database construction occurs. It is too late to correct problems in data redundancy, integrity or scalability. Being able to identify them early in the analysis phase enables teams to correct the problem before the implementation phase is reached, thus saving investment time and resources.
- Supporting compliance
Because existing data privacy regulations are becoming more stringent, data modeling in support of regulatory compliance and governance is becoming critical. Another good thing is the documentation of the data model, which explains the data ownership, usage policies, and data lineage.
- Responsive procedure
Data modeling is not a one-time activity but rather an activity that changes over time and as new business requirements and new technologies are introduced. Models are flexible with the introduction of new data sources, the modified requirements of users, and new analytics tools.
- Driving agility
Companies that resort to excellent data modeling strategies obtain the capacity to develop sound, sustainable, and expandable data systems. With such systems, teams can be armed to harness these data assets to be placed in a better position to make decisions as well as innovate.