Data modeling is the process of creating a visual representation of data within an information system to illustrate the relationships between different data types and structures. The goal is to model data at conceptual, logical, and physical levels to support business needs and requirements. Conceptual models provide an overview of key entities and relationships, logical models add greater detail, and physical models specify how data will be stored in databases. Data modeling benefits include reduced errors, improved communication and performance, and easier management of data mapping.

1. Data Modeling
Data modeling is the process of creating a visual representation of either a whole
information system or parts of it to communicate connections between data points and
structures.
Goal Of Data Model
The goal is to illustrate the types of data used and stored within the system, the
relationships among these data types, the ways the data can be grouped and organized
and its formats and attributes.
Data models are built around business needs.Rules and requirements are defined
upfront through feedback from business stakeholders so they can be incorporated into
the design of a new system or adapted in the iteration of an existing one.
Data can be modeled at various levels of abstraction.
The process begins by collecting information about business requirements from
stakeholders and end users.
These business rules are then translated into data structures to formulate a concrete
database design.
A data model can be compared to a roadmap, an architect’s blueprint or any formal
diagram that facilitates a deeper understanding of what is being designed.
Data modeling employs standardized schemas and formal techniques. This provides a
common, consistent, and predictable way of defining and managing data resources
across an organization, or even beyond.
Ideally, data models are living documents that evolve along with changing business
needs. They play an important role in supporting business processes and planning IT
architecture and strategy. Data models can be shared with vendors, partners, and/or
industry peers.
Types of data models
Like any design process, database and information system design begins at a high
level of abstraction and becomes increasingly more concrete and specific.
Data models can generally be divided into three categories, which vary according to
their degree of abstraction.
The process will start with a conceptual model, progress to a logical model and
conclude with a physical model.

2. Conceptual data models.
They are also referred to as domain models and offer a big-picture view of what the
system will contain, how it will be organized, and which business rules are involved.
Conceptual models are usually created as part of the process of gathering initial
project requirements.
Typically, they include:
a) entity classes (defining the types of things that are important for the business to
represent in the data model),
b) their characteristics and constraints,
c) the relationships between them,
d) relevant security and data integrity requirements.

3. Logical data models.
They are less abstract and provide greater detail about the concepts and relationships
in the domain under consideration.
One of several formal data modeling notation systems is followed. These indicate data
attributes, such as data types and their corresponding lengths, and show the
relationships among entities.
Logical data models don’t specify any technical system requirements.

4. Physical data models.
They provide a schema for how the data will be physically stored within a database.
They offer a finalized design that can be implemented as a relational database,
including associative tables that illustrate the relationships among entities as well as
the primary keys and foreign keys that will be used to maintain those relationships.
Benefits of data modeling
Data modeling makes it easier for developers, data architects, business analysts, and
other stakeholders to view and understand relationships among the data in a database
or data warehouse. In addition, it can:
 Reduce errors in software and database development.
 Increase consistency in documentation and system design across the enterprise.
 Improve application and database performance.
 Ease data mapping throughout the organization.
 Improve communication between developers and business intelligence teams.
 Ease and speed the process of database design at the conceptual, logical and
physical levels.
IMPORTANCE OF DATA MODELS
 Modelling can guide your exploration:
 It can help you figure out what questions to ask
 It can help to reveal key design decisions

5.  It can help you to uncover problems
 Modelling can help us check our understanding
 Reason about the model to understand its consequences
 Does it have the properties we expect?
 Animate the model to help us visualize/validate software behavior
 Modelling can help us communicate
 Provides useful abstractions that focus on the point you want to make
without overwhelming people with detail
DATA MODELING – THE PROCESS
Data modeling is a method used to define and analyze data requirements
needed to support the business processes of an organization; it defines data
elements, their structures and relationships between them.
It is the process of creating a data model for the data to be stored in a
Database which helps in the visual representation of data and enforces
business rules, regulatory compliances, and government policies on the data.
Data Modelling Process.
Step 1
Initially, the data requirements are recorded as a conceptual data model
which is essentially a set of technology independent specifications about the
data and is used to discuss initial requirements with the business stakeholders.
Step 2
The conceptual model is then translated into a logical data model, which
documents structures of the data that can be implemented in databases.
Implementation of one conceptual data model may require multiple logical
data models.
Step 3
The last step in data modeling is transforming the logical data model to a
physical data model that organizes the data into tables, and accounts for
access, performance and storage details.
DATA MODEL METHODOLOGY
The two major methodologies used to create a data model are
 The Entity‐ Relationship (ER) approach.
 The Object‐ oriented Model.
1. Entity‐ relationship Model
Entity – Relationship model (ER model for short) is the conceptual design of a
database that includes its entities and relationships.
In other words, it is an abstract way to describe a database. It usually starts with a
relational database, which stores data in tables.
Some of the data in these tables point to data in other tables.

6. EXAMPLE
An Aerodrome entry in the database could be related to several Runways that belong
to this Aerodrome.
The ER model would say that the “Aerodrome” is an entity, and each “Runway” is an
entity
These entities might have a list of properties called “Attributes” like the Identifier,
Name, Length, Width, etc. and the relationships between the Aerodrome and the
Runway is in one direction “having Runways” and from the other direction “situated
at one Aerodrome”.
Diagrams created to design these entities and relationships are called entity–
relationship diagrams or ER diagrams.
Figure for Entity-Relationship Diagram
2. Object‐ oriented Model
Object‐ oriented modeling (OOM), also called object‐ oriented programming (OOP)
is a modeling paradigm mainly used in computer programming.
The object‐ oriented paradigm assists the programmer to address the complexity of a
problem domain by considering the problem not as a set of functions that can be
performed but primarily as a set of related, interacting Objects.
The modeling task then is specifying, for a specific context, those Objects (or the
Class the Objects belongs to), their respective set of Properties and Methods, shared
by all Objects members of the Class.
The Model description or Schema may grow in complexity to require a Notation.
Many notations have been proposed, based on different paradigms, diverged, and
converged in a more popular one known as UML (Unified Modeling Language).
Let’s take the same example as in the entity‐ relationship model about the Aerodrome
and Runway and consider them a Class of object as defined in the UML, where they
will also be defined by a list of properties and constrained by a data type, besides the
two classes they also have a relationship as shown in next figure where a Class
diagram is shown.

7. Figure for Class Diagram
Both object‐ oriented modeling and Entity‐ relation model are quite common in
software today. Since relational databases don't store objects directly, there is a
general need to bridge the two worlds by using different approaches to cope with this
problem like Object‐ relational mapping or Object Database methodology, subjects
which are out of the scope of this material.