Understanding Data Modelling Techniques: A Comprehensive Guide 101

ganizations 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 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.

Prerequisites

• A general understanding of databases
• A generic idea of analytics workflows

What is Data Modelling?

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. 

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.

What are the Steps To Consider During Data Modelling?

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

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

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 minimizing data privacy risks.

Object-Oriented Data Modelling

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, 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 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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