This document provides an overview of data modeling techniques. It discusses the types of data models including conceptual, logical and physical models. It also outlines some common data modeling techniques such as hierarchical, relational, entity-relationship, object-oriented and dimensional modeling. Dimensional modeling includes star and snowflake schemas. The benefits of effective data modeling are also highlighted such as improved data quality, reduced costs and quicker time to market.

1. Understanding Data Modelling Techniques:
A Comprehensive Guide 101
Ratan Kumar • February 4th, 2022
As organizations are collecting a plethora of information, organizing Big Data for business needs
has become increasingly challenging. Often companies struggle to harness the potential of data as
gathered information is not structured to support Data Analytics. Since data comes from different
sources, without devising proper Data Modelling Techniques, organizations fail to find
relationships among data points for garnering better insights. To mitigate such challenges,
companies are leveraging Data Modelling techniques.
With effective Data Modelling planning, organizations can simplify the entire analytics process
within organizations for business growth. However, companies need to understand their business

2. requirements and model data in a way that can facilitate better decision-making.
In this article, you will understand Data Modelling Techniques and how companies can benefit
from the same.
Table of Contents
Prerequisites
What is Data Modelling?
What are the Types of Data Models?
Conceptual Data Models
Logical Data Models
Physical Data Models
What are the Steps to Consider During Data Modelling?
Identify the Entities and Properties
Identify Relationships among Entities and Attributes
Assign Keys
What are the Types of Data Modelling Techniques?
Hierarchical Data Modelling
Relational Data Modelling
Entity-Relationship (ER) Data Modelling
Object-Oriented Data Modelling
Dimensional Data Modelling
Star Schema
Snowflake Schema
What are the Benefits of Data Modelling Techniques?
Data Quality

3. Reduced Cost
Quicker Time to Market
Conclusion
Prerequisites
A general understanding of databases
A generic idea of analytics workflows
What is Data Modelling?
Image Source: Intellipat
Data Modelling is a process of structuring data collected from disparate sources to allow decision-
makers to make informed decisions with analytics. With Data Modelling, organizations illustrate the
types of data used, relationships among information, and organization of data. In other words, Data
Modelling is a technique to optimize data for streamlining information flow within organizations for
various business requirements.
Build for enhancing analytics, Data Modelling includes formatting of data and its attributes, building
relationships among information, and grouping data. This not only assists companies in
maintaining consistency but also enhances the predictability of use cases they can carry out.
Without proper Data Modelling, organizations fail to accomplish their business goals due to the
absence of a well-defined roadmap for Data Analytics.

4. What are the Types of Data Models?
As Data Modelling techniques are incorporated within organizations based on business
requirements, it is essential to align them with database design schemas. Consequently, it is vital
to ensure all three aspects — Data Modelling, business requirements, and database design
schema — are taken into account while devising a strategy for superior data management and
analytics workflows. However, before embracing Data Modelling techniques, the below
methodologies are incorporated for a successful implementation:
Conceptual Data Models
In conceptual data models, business requirements are assimilated to define the types of data
needs, collection procedures, and security demands
Logical Data Models
This model is highly prominent with companies that are heavily involved in data warehousing.
Logical data models help organizations formulate data consolidation and segregation for
simplifying Data Analytics.
Physical Data Models
With physical data models, companies finalize the relation among tables and deploy the right
databases.
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What are the Steps To Consider During Data Modelling?

6. Before laying out different types of Data Modelling techniques, organizations have to carry out due
diligence by evaluating the requirements of various departments and other stakeholders. Below
are a few methods that can channel out the roadmap for Data Modelling:
Step 1: Identify the Entities and Properties
Consider a business operation and assimilate the data requirements around the process. Then
conceptualize how you will pull relevant data from different sources to obtain several properties —
data that describes the entities — for data consolidation.
Step 2: Identify Relationships among Entities and Attributes
After devising a plan for gathering data, the next step involves creating a blueprint of the
relationship among entities. This allows organizations to organize data, thereby assisting in ETL
processes. Identifying the right relationship among entities and attributes is the key to working
with structured data for augmenting analytics.
Step 3: Assign Keys
To avoid data redundancy, assigning keys in tables assists in connecting disparate tables in
databases. Assigning keys not only helps in creating a relationship but also provides flexibility
while data enrichments as it simplifies connecting more data from secondary or external sources.
What are the Types of Data Modelling Techniques?
Hierarchical Data Modelling

7. Image Source: MariaDB
Developed by IBM in 1960, hierarchical Data Modelling is a tree-like structure, which has one root
or parent connecting to different children. The parent data is in direct association with child data
points, making it a one-to-many relationship. Although simple, hierarchical Data Modelling is not
suitable for complex structures. As a result, hierarchical Data Modelling is not widely used in the
data-driven world. Today, data analyses are performed by evaluating relationships among different
data points, thereby requiring a many-to-many relationship structure. However, with a one-to-many
relationships model, it becomes strenuous for companies to gain an in-depth understanding of
collected information.
Relational Data Modelling

8. Image Source: Polarwinco
Relationship Data Modelling is the most well-known technique used in databases to support
analytics initiatives. Data in relational Data Modelling is organized in tables that are in relation to
each other. Proposed in 1970 by Edgar F. Codd, relational databases are still the go-to Data
Modelling for complex data analysis. Organizations use structured query language (SQL) to obtain
and record data in the form of tables while maintaining the relationship intact for better
consistency and data integrity.
Entity-Relationship (ER) Data Modelling
Entity-relationship Data Modelling was introduced by Peter Chen in 1976 that revolutionized the
computer science industry. Entity-relationship models are a logical structure where the relationship
among data points is created based on specific software development requirements. Unlike
relational Data Modelling techniques, entity-relationship Data Modelling is designed to support
business processes in a particular order. Even if two datasets can have numerous relationships,
entity-relationship is only created based on the data points needed for accomplishing a task while

9. minimizing data privacy risks.
Object-Oriented Data Modelling
Image
Source:
Wikipedia
Object-Oriented is used to represent the real world by grouping objects into classes hierarchy.
This structure has been used with several object-oriented programming languages that allow
foundational features like encapsulation, abstraction, and inheritance. Object-oriented Data
Modelling techniques are used for representing and working with complex analyses.
Dimensional Data Modelling
Introduced by Ralph Kimball in 1996, dimensional Data Modelling is leveraged to optimize data
retrieval from data warehouses. In dimensional Data Modelling, data are represented in cubes or
sets of tables to allow slicing and dicing for better visualization or analysis. With dimensional Data
Modelling, uses can carry out in-depth analysis by assessing data based on different viewpoints.
Organizations implement two types of dimensional Data Modelling techniques — star schema and
snowflake schema.
Star Schema
In this modeling, facts and dimensions are used to represent the relations, where facts are
measurable items and dimensions are reference information. Every measurable item is surrounded
by its associated dimensions, making it look like a start.
Snowflake Schema
The snowflake schema is an extension of the start schema as it has multiple layers for dimensions,

10. allowing complex data analysis.
Benefits of Data Modelling Techniques
1. Data Quality
For any data science project, almost 80 percent of the time is lost in data wrangling. However, with
Data Modelling, you define business problems and then plan the data collection process
accordingly. This not only streamlines the entire data flow but also enhances the data quality.
Companies obtain a blueprint by planning to implement Data Modelling techniques, which
empowers the data analysts in extracting data without worrying about the data quality. Desired
Data Modelling has the potential to expedite data analysis by creating relationships among data
points.
2. Reduced Cost
By implementing Data Modelling according to the business requirements, you are more likely to
follow the defined roadmap for data collection and analysis. This will reduce the cost since the
needs of businesses are taken into account while deploying the Data Modelling techniques. Often
companies with poor Data Modelling techniques have to revamp their data collection process,
thereby increasing operational costs. However, if an organization has the right Data Modelling
strategy from the very beginning, it not only reduces costs but also expedites analytics.
3. Quicker Time to Market
By deploying the perfect Data Modelling techniques according to the needs within departments,
companies can reduce the time for bringing products and services. A perfect Data Modelling
technique can eliminate several bottlenecks that companies witness while deploying data
strategies.
Conclusion
Data Modelling techniques are a part of data strategy for organizations to simplify the entire
analytics process across departments. With proper Data Modelling Techniques, companies can

11. gain operational resilience while ensuring the quality of insights for making informed decisions.
Failing to incorporate desired Data Modelling can lead to operational inefficiencies due to
changing requirements of organizations. Consequently, organizations must devise database
design schema to implement the right modeling techniques and support Data Analytics.
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