Data is transforming
the way the globe works. It could be a study on a cure for a disease, a
company’s revenue strategy, efficient building construction, or targeted ads on
your social media page, all because of data.
This data directs to
information that is machine-readable as resisted to human-readable. Let’s take
an example, customer data is meaningless to the product team unless it points
to specific product purchases. Similarly, the marketing team will not use this
data if the IDs are not related to particular price points during purchase.
This is where data
modeling comes in. It is a process that assigns relational rules to data. A
data model simplifies data into actionable information that organizations can
use for decision-making and strategy.
Before diving deeper
into data modeling, let’s first understand the data model in detail.
What is a data model?
A data model is
defined as an abstract model that organizes data description, data semantics,
and data consistency constraints. A data model emphasizes what data is needed
and how it should be managed rather than what operations will be performed. A
data model is like an architect’s blueprint that helps create conceptual models
and relationships between data items.
There are two types
of data modeling techniques
“ Entity Relationship (E-R) Model.
“ UML (Unified Modeling Language)
What is data-modeling ?
Data modeling in software engineering simplifies a software system’s
diagram or data model using specific formal techniques. It involves expressing
data and information through text and symbols. A data model provides a
blueprint for building a new database or reengineering legacy applications.
In light of the
above, this is a critical first step in defining the structure of available
data. Data modeling is the creation of data models in which data associations
and constraints are described and eventually coded for reuse. Conceptually
represents data with diagrams, symbols, or text to visualize
interrelationships.
Data Modeling thus
helps increase consistency in naming, rules, semantics, and security. This, in
turn, improves data analysis. Emphasis is placed on the need for availability
and organization of data regardless of the way it is applied.
Why use data modeling?
The prior goal of
using a data modeling is to:
“ Secures that all data objects required by the
database are accurately represented. Omitting data will result in erroneous
reports and incorrect results.
“ A data model helps design a database at the
conceptual, physical, and logical levels.
“ The data model structure helps define
relational tables, primary and foreign keys, and stored procedures.
“ It provides a precise picture of the
underlying data and can be used by database developers to make a physical
database.
“ It is also valuable for recognizing missing
and redundant data.
“ Although the initial creation of the data
model is laborious and time-consuming, upgrading and maintaining your IT
infrastructure is cheaper and faster in the long run.
The best data modeling tools are listed below:
“ Elixir data
“ ER/Studio
“ DbSchema
“ HeidiSQL
“ Toad Data Modeler
“ builder
“ Data
modeling is formulating data
in a structured format in an information system. Below are specific practical
uses of related tools in any sector or industry.
“ Data modeling helps create a robust design
with a data model that can display all of an organization’s data on the same
platform.
“ The data model ensures that all data objects
required by the database are represented or not.
“ A database at the logical, physical, and
conceptual levels can be designed using the help data model.
“ A data model can define relational tables,
foreign keys, and primary keys.
“ Data
modeling tools help improve
data quality.
“ The data model provides a clear picture of the
business needs.
“ Duplicative data and missing data can be
identified using data models.
“ In data models, all essential data is accurately
represented. The chances of incorrect results and false reports are reduced
because the data model has reduced missing data.
“ Data models create a visual representation of
data. With its help, data analysis improves. We get a data image developers can
use to create a physical database.
“ Better consistency can be qualified using a
data model across all projects.
“ The data model is quite time-consuming, so
maintenance is cheaper and faster.
What are the types of data modeling?
Data modeling helps
to create a conceptual model and relationship between items. Basic data
modeling techniques involve working with three typesof the data modeling.
Conceptual model
A conceptual data
model depicts the data needed to support business processes. It also tracks
business events and maintains related performance metrics. A conceptual model
defines what the system contains. This type
of data modeling focuses on retrieving the data used in the business rather
than the processing flow. The primary purpose of this data model is to organize
and define business rules and concepts. For example, it helps marketers view
any data such as market data, customer data, and purchase data.
Logic model
The map of rules and
structures in a logical data model includes the required data such as tables,
columns, etc. Data architects and business analysts create a logical model. We
can use the logical model to change it into a database. This type of data modeling is always
present in the root package object. This data model helps form the basis for
the physical model. No secondary or primary key is defined in this model.
Physical data model
A physical data model
describes the implementation using a specific database system. It defines all
the components and services needed to build the database. It is created using a
database language and queries. The physical data model conveys each table,
column, and constraint as primary key, foreign key, NOT NULL, etc. The primary
work of the physical data model is to form a database. This model is created by
a database administrator (DBA) and developers. This type of data modeling provides us with the abstraction of databases
and helps create a schema. This model describes a specific implementation of
the data model. The physical data model enables database column keys,
constraints, and RDBMS functions. A database that uses a graph architecture for
semantic query with nodes, edges, and properties to represent and store data.
What are data modeling techniques?
Below are 5 different
types of techniques used to organize data:
Hierarchical technique
A hierarchical model
is a tree structure. There is one root node or parent node, and the other child
nodes are sorted in a particular order. However, the hierarchical model is now
very rarely used. This model can be used for relationships between models in
the real world.
Object-oriented model
An object-oriented
approach is the creation of objects that contain stored values. The
object-oriented model communicates while keeping data abstraction, inheritance,
and encapsulation.
Network technology
The network model
provides a flexible representation of objects and the connections between these
entities. It has a schema feature to represent data as a graph. An object is
defined inside a node, and the relationship between them is an edge, allowing
them to support multiple parent and child records in a generalized way.
Entity relationship model
An ER
(Entity-relationship model) is a high-level relational model that defines data
elements and relationships for entities in a system. This conceptual design
delivers a better view of the data, which allows us to understand it quickly.
In this model, the entire database is described in an entity-relationship
diagram which contains entities, attributes, and relationships.
Relational technique
Relational is used to
describe various relationships between entities. And there are different sets
of relationships between entities, such as one-to-one, one-to-many,
many-to-one, and many-to-many.
Advantages and disadvantages of data modeling:
Advantages of data modeling:
“ The main goal of data model design is to
ensure that the data objects offered by the functional team are accurately
described.
“ The data model should be straightforward
enough to be used to build the physical database.
“ Data model information can define the
relationship between tables, primary and foreign keys, and stored procedures.
“ The data model helps companies communicate
within and between organizations.
“ A data model helps document data mapping in
the ETL process.
“ Help identify the suitable data sources to
populate the model.
Disadvantages of data modeling:
“ To create a data model, you should know the
properties of the stored physical data.
“ It is a navigation system that creates complex
application development and management. So it requires knowledge of
biographical truth.
“ Even more minor changes in the structure
require modification in the entire application.
“ There is no set language for manipulating data
in a DBMS.
Winding up
In short, data
modeling helps in a visual representation of data. Data models are created
during a project’s design and analysis phase to meet these application
requirements. This is what Data Modeling holds for us.
