In the realm of computer science, a logical data model is the accurate representation of a company’s data. These data need to be logically represented because later on they will be the basis for data modeling. Data modeling in turn will be the basis for database implementation as the computer needs to understand business entities and activities from a digital perspective.
At the logical data model, a data modeler needs to describe all data in the most detailed way possible. This should be regardless of how the physical database will be implemented. The logical data model includes identification of all entities are relationships among them. It also lists all the attributes for each entity which is being specified.
In fact, the steps for designing a logical data model are:
1. Identifying all the entities based on business activities
2. Specifying the primary keys for all identified entities
3. Finding and defining all relationships between different entities
4. Finding attributes for each entity
5. Resolving many to many relationships
A data attribute is an instance or occurrence of any attribute type. A data attribute value is a characteristic of or any fact describing the occurrence of an entity. For instance, an entity’s color maybe "red" or "blue" and other color that correctly describes the entity.
Each type entity will have one more data attributes. In logical data modeling, data attributes should always be cohesive from the perspective of the domain. This is often called a judgment call for the data modeler or data architect. Getting to the deepest level of detail can make a real significant impact on the development and maintenance efforts during the future of the implementation.
Data attributes will always exist for an entity regardless of whatever is being represented by the entity in the real business situation. For instance in the business scenario, a logical data model may have an entity of Customer. The data attributes to the Customer entity may include but not limited to first name, middle name, last name, address, age, gender, profession, and many more.
Data processing is about data attribute values. These data attribute values represent the most tangible or least abstract areas of data processing and they are the core of any information management systems.
In relational database management systems, data attributes should be managed well so that redundancy of these data will not affect the whole database system in a negative way. Determining the proper uses of data attributes is very important in database normalization. Data normalization is the process wherein the data attributes within the data model are organized in a cohesive way with the entity types so as to increase the performance of the database by reducing processing time by eliminating redundant data.
As a general rules, there are 3 of data normalization.
The first normal form (1NF) states that any entity type is in the first normal form when it contains no repeating or redundant groups of data.
The second normal form (2NF) states that an entity is in the type of the second normal form when it is in the 1NF and when all of the non key attributes are fully dependent on the primary key.
The third normal form (3NF) states that any entity is in the third normal form when it is in the 2NF and when all of its attributes are directly dependent on the primary key.
As can be seen here, knowing the correct data attribute of an entity and how arrange them in tables and defining the correct relationships can give a database performance a great improvement.