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LOGICAL
DATA MODEL
December 2023
ABDUL AHAD, AAHADQJ@GMAIL.COM
Data Modelling 101

WHAT IS LOGICAL DATA MODEL
A logical data model establishes the structure of data elements and the relationships among them.
It is independent of the physical database that details how the data will be implemented. The
logical data model serves as a blueprint for used data. The logical data model takes the elements of
conceptual data modeling a step further by adding more information to them.
The logical data model incorporates all of the elements of information that are vital in the running
of the day to day business.

NEED OF LOGICAL DATA MODEL
Given that data embodies the most crucial aspect of any application, program, or system, quality
data processing and storage systems must be built upon a strong and accurate underlying data
structure. A sound data structure gives application developers the freedom to design the best
possible user interface, processing system, or statistical analysis and reporting set-up.
No matter how elegant or technical your system, it has to meet requirements, follow rules, and
serve the purposes of the business or enterprise it is built for—or else it is of no practical use.
Therefore, logical data modeling brings together the two most vital basics of application
development:
Business requirements
1.
Quality data structure
2.

COMPONENTS
OF A LOGICAL DATA MODEL
01 ENTITIES
Entities: Each entity represents a set of things, persons, or concepts
relevant to a business
02 RELATIONSHIPS
Every relationship represents an association between two of the above
entities
03 ATTRIBUTES
Each attribute is a descriptive piece, characteristic or any other
information that is useful to further describe an entity

CHARACTERISTICS OF A LOGICAL DATA MODEL
A logical data model can describe the data needs for each individual project. Yet, it is designed
to seamlessly integrate with other logical data models should the project demand it to do so.
A logical data model can be developed and designed independently from the database
management system. The type of database management system does not affect it that much.
Data attributes contain data types with exact length and precisions.
In logical data modeling, no primary or secondary key is defined. At this level of data modeling,
it is required to verify and tweak connector details that were set prior to defining relationships.
A logical data model is like a graphical representation of the information requirements of a
business area. It is not a database or database management system itself.
A logical data model is independent of any physical data storage device, such as a file system.
A logical data model must be designed to be independent of technology, so as not to be
affected by the rapid changes in technology.

DATA MODELING TECHNIQUES
Entity Relationship (E-R) Model
UML (Unified Modelling Language)
Logical data modeling belongs to the entity relationship model, built using an Entity
Relationship Diagram (known as ERD), a standard modeling technique used as a
communication tool by data modelers worldwide. Within it are the complete set of business
requirements but not technical components.

ADVANTAGES OF A LOGICAL DATA MODEL
As data remains stable over time, a logical data model is also a stable one and highly conducive to data re-
use and physical data sharing, which ultimately leads to reduced storage of redundant data.
Components of a logical data model can be recycled, re-used, and adapted as more teams weigh in with
their (often changing) needs.
Costs associated with building and maintaining a logical data model are offset in the long run by the
advantages it confers, not least by identifying and integrating all business needs and rules at the outset.
Components of the building process, namely, design, coding, testing, and deployment go faster, as a direct
result of the integration and clarification of business rules.
Having a logical data model in place makes it easier, and therefore cost effective, to make changes, correct
mistakes, or enter missing data during the development life cycle itself prior to implementation.
User requests for making changes can be minimized by being proactive.
Logical data models can be used for impact analysis, as each and every business process plus rule is
connected within it.
As objects in the logical data model bear textual definitions in business language, it makes it easier to
maintain and access system documentation.

HOW DOES A LOGICAL DATA MODEL WORK?
Logical data models serve as an abstraction layer, defining the relationships between different data elements,
entities, and attributes. Unlike a physical data model, which is specific to a particular database system, a logical
data model focuses on the business concepts and rules that govern the data.

Entities, Relationships, And Attributes
Entities are the fundamental building blocks of a logical data model, representing objects or concepts—
customers, products, or orders, for example. Relationships define how these entities are connected or
associated with each other, while attributes describe the characteristics or properties of the entities. In the
example below, the logical data model illustrates a set of related tables connected by primary key (PK) and
foreign key (FK) relationships.

Normalization
Normalization is a key concept in logical data modeling that involves the organization of data to reduce
redundancy and improve data integrity. The goal of normalization is to eliminate data anomalies—update,
insert, or delete anomalies, for example—by structuring the data in a way that minimizes duplication. The
processes and stages of normalization involve breaking down large tables into smaller, more manageable tables
and establishing relationships between them.

BENEFITS
OF LOGICAL DATA MODELING
Thynk Unlimited
01 Improved Data Comprehension
Provides is a clear and comprehensive view on data. By mapping out the
relationships between different data elements in easy-to-
understand/minimal notation, a range of stakeholders
02 Better Communication
Logical data models serve as the common language for bridging the
communication gap among various stakeholders involved in the data
management process.
03 Change Management
Change is of course inevitable, and logical data models—when designed
well—allow organizations to adapt and evolve more efficiently and with
greater agility. From accommodating new business rules and modifying
existing processes to integrating new
04 Enhanced Data Quality
By promoting normalization and adherence to data modeling best
practice, logical data models contribute to improved data quality across
an organization’s data estate. Logical data modeling practices like
reducing redundancy and enforcing relationships between entities
helps to maintain data integrity, as well as minimize errors and
inconsistencies caused by duplicate or conflicting information.

LOGICAL DATA MODEL - EXAMPLES
Logical data models can be used in a wide range of applications.
The following examples show how the logical data model paradigm can be used from the perspective of
different domains.
Healthcare Management—In a healthcare management system, a logical data model might include entities
such as “Patient,” “Doctor,” “Appointment,” and “Medical Record.” Relationships could include “Doctor treats
Patient,” “Patient schedules Appointment,” and “Medical Record corresponds to Patient.” Attributes for the
“Patient” entity might include “PatientID,” “Name,” and “Date of Birth.”
Financial Services—In the financial services sector, a logical data model could encompass entities like
“Account,” “Transaction,” and “Customer.” Relationships might include “Customer owns Account” and
“Transaction involves Account.” Attributes for the “Account” entity could include “AccountID,” “Balance,” and
“Account Type.”

THANK YOU
FOR YOUR NICE ATTENTION

Reference:
https://www.datamation.com/big-data/raw-data/
https://agiledata.org/essays/datamodeling101.html
https://budibase.com/blog/data/how-to-create-a-data-model/

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