Data Modelling

Data modeling can be defined as the process of defining the real life entities of a business in a system or a database. This is a very important process in the creation of database structures that are understandable by the system architects and other stakeholders in the system. Data modeling offers an easy way of depicting the structure of data that is contained within a database and how it is related to make sure that the database is as efficient as possible and can cater for the needs of the business.

Purpose and Importance of Data Modeling

The most important objective of data modeling is to ensure that the data is well sorted, shared and used effectively in order to meet the desired goals and objectives in any given organization. Data modeling is a useful tool that graphical representation of data elements and their relationships and allows avoiding some problems that may occur during the implementation of the model. This approach is preventive in nature and helps in avoiding the occurrence of errors as well as makes the database design efficient by increasing its capacity and making it fit to the organizational goals.

Data models are usually depicted with some standard graphical notation of symbols which show entities, attributes, and relationships. These diagrams are used to design and alter the database systems and help in achieving a common perspective about the data structure among all the concerned individuals.

Types of Data Modeling

Conceptual Data Modeling (CDM) is the process of building an initial and rather high level view of the structure of the data. This kind of modeling is usually applied in the early stage of a project with the aim of developing a consensus view of the data foundation. This offers an general perspective of the data environment without going in to details of any certain system.

Logical Data Modeling (LDM) is the enhancement of the conceptual model where it identifies all the entities that are present in the system and the characteristics and connection of these entities. The model proposed here is not linked to any particular DBMS however it suggests a complete DB schema that can be instantiated on any system. It identifies the data requirements, types of data, and constraints, which provides a clear guideline of the database design.

Physical Data Modeling (PDM) is the actual creation of the database in the physical sense. It includes defining of tables, columns, data types, indexes and table relationships according to the requirements and specifications of the system. Physical data modeling incorporates performance, storage and retrieval issues so as to ascertain that the database is well equipped to perform its function.

Specialized and Other Types of Data Modeling

Besides these two primary types of data modeling there are other subcategories of data modeling which are used in specific context like data warehousing and business intelligence. Dimensional modeling is one such method, favoured in these fields to form a structure of the database that will enable the storage of data that can be queried and reported on in a detailed manner.

These are the older methods for organizing data which is displayed in the form of trees or graphs which are known as Hierarchical and Network Data Models. These models were typical to the early database systems and are not as popular as the present day models. Nevertheless, they are still helpful in some old-fashioned systems and unique solutions.

Conclusion

Data modeling is one of the most important activities in the development of database and its structure. This concept of data modeling prescribes the physical and logical layout of data in a system thus making it possible to design databases that are efficient, expandable and relevant to organizational objectives. This process of modeling data in the conceptual, logical or the physical form of data modeling gives a clear understanding of how to manage data to ensure that it meets the needs of the organization as well as for the development of systems that are useful for the organization. By planning and designing the system, data modeling reduces the possibilities of errors, improves communication, and results to the development of effective systems.