What is a Logical Data Model?
A logical data model defines the data structure and relationships between them. It is independent of the physical database that details the implementation of the data. The logical data modeling serves as a guideline for data usage. The logical data models take the elements of conceptual data modeling one step further by providing more information.
The logical data model includes all the information necessary for the operation of a day-to-day business.
Components of a Logical Data Model
Three components make up a logical data model:
- Entities: Each entity is a collection of objects, people, or concepts that are relevant to a business.
- Relationships: Each relationship is an association between the entities mentioned above
- Attributes: Each attribute describes a characteristic, piece of information, or other information that can be used to describe an entity.
Each component of a logical model is given a name along with a textual description. These are used to continuously document business rules and outline information requirements. However, the above components are only meant to describe business requirements. These components do not address how business requirements are stored, processed, or implemented.
Need for a Logical Data Model
Data is the most important aspect of any program, application, or system. Therefore, it is essential that quality data processing and storage systems are built on a solid and accurate data structure. Developers have the freedom to design the most effective user interface, processing system, or statistical analysis and reporting set up by using a sound data structure.
It doesn’t matter how complex or elegant your system is, it must meet the requirements of the enterprise or business it was created for. Logical data modeling combines the most important elements of application development.
- Business requirements
- High-quality data structure
Characteristics of Logical Data Model
These are the most important features of a logical data model.
- A logical data modeling can be used to describe the data requirements for every project. It can also be used to integrate with other logical models if required by the project.
- You can create and design a logical data model independently of the database management system. It doesn’t matter what type of database management system it is.
- Data attributes are data types that have exact lengths and precisions.
- No primary or secondary key can be defined in logical data modeling. It is necessary to check and
- adjust connector details that were previously set before defining relationships.
- A logical data model can be described as a graphic representation of information requirements for a particular business area. It does not contain a database or a database management system.
- A logical data model can be used independently of any physical storage device such as a filesystem.
- So that technology doesn’t affect a logical data model, it must be completely independent.
Details of Logical Data Modeling
In a nutshell, a data model is a collection of data specifications and diagrams that help to explain data requirements and other related designs. There are generally three types of data modeling activities and types.
Conceptual Data Model vs Logical Data Model vs Physical Data Model
Conceptual Data Model
The data model defines the core features of the system. Data architects and business stakeholders are often the ones who create conceptual models of data. They do this to organize and define different business concepts and rules, and to determine the scope and parameters.
Logical Data Model
The logical data model is used to determine how a system should be implemented, regardless of which database management system it uses. A logical data model is usually created by business analysts and data architects. A logical data model is a highly technical map that outlines the data structures and rules.
Physical Data Model
The physical data model describes how the system will work and the factors that will be used to manage the databases. This model is usually created by developers. This model is used to describe how the database will be used for business purposes.
In general, conceptual data modeling and logic data modeling are both “requirements analysis” types of activities. Physical data modeling, however, is considered a data model design activity.
A logical data modeling is a basis for a physical model. It incorporates business requirements and gathers metadata. Standard techniques and data modeling notations can be used to create logical data models.
Data modeling is a process that organizes data semantics and describes data. It also addresses data consistency issues. It can be compared to an architect’s drawing or building schematic. This is the foundation for conceptual modeling and establishes relationships between different data components.
Two types of data modeling techniques are available:
- Entity Relationship (E–R) Model
- UML (Unified Modelling Language)
Logical data modeling is part of the entity-relationship model. It was built using an Entity Relationship diagram (also known as ERD), which is a standard model technique that data modelers use as a communication tool. It contains the entire set of business requirements, but not technical components.
Benefits of Logical Data Models:
- A logical data model, which is stable over time, is highly conducive for data re-use, physical data sharing, and data stability. This ultimately reduces redundant data storage.
You can reuse, re-use, or adapt components of a logical model to meet your (often changing!) needs.
- The costs associated with maintaining and building a logical database modeling can be offset by the benefits it offers, not only by identifying all business requirements and rules and integrating them at the beginning.
- As a result of the integration of business rules, the speed of components of the building process, such as design, coding, and testing, is increased.
- A logical data model makes it easy and cost-effective to make changes, correct errors, or enter missing information during the development cycle.
- It is possible to reduce user requests for changes by being proactive.
- Logical data models are useful for impact analysis because each business process and the rule is linked within them.
- It is easier to maintain and access documentation because objects in the logical model have textual definitions in business languages.
What happens if a Logical Data Model Is Not Created?
There can be problems. If users aren’t reminded that data is the main ingredient in a system’s design, they can become distracted by processes and activities. A data model that is purely built on physical workflows misses the opportunity to represent business requirements.
Designers who create tables and files without having data elements defined according to business requirements are more likely to have poor organization and a weak underlying structure. Developers must be proactive rather than reactive when attempting to incorporate additional data elements from report or screen layouts. The output is a mixture-and-match entity, difficult to use or maintain, with errors or excessive text, without system documentation, and potentially unusable.
Logical data modeling defines the structure of data elements based upon fundamental business requirements and the relationships between them. This means that there are many missed opportunities to improve business processes. Developers end up automating current procedures or recreating legacy systems using a newer technology platform that may eventually become obsolete.
Logical data modeling allows data analysts the freedom to think independently of new technology and can focus on improving business processes.
A logical data model is a crucial and essential component of any application development project. This is an essential step that should be taken before database design.