What Is Data Modeling? Process, Tools, and Best Practices

Introduction

Data modeling refers to the practice of creating a data model for an entity set. It has been around more than four decades but still, it plays a crucial role in maintaining the quality and consistency; not only that– it also helps streamline processing if done correctly. Today, it is the foundation of modern data warehousing and business intelligence. Through this blog, we will understand how data modelling is done actually and type of it, tools that are used for doing data modeling process these days mostly by the organizations to structure your data effectively.

What is a Data Model?

A data model is a conceptual representation of the structure your datasets take on. As a blue print it defines the relationship between different data entities. Another way of making sure the handling of data is through something called a Diagram. Data models form the foundation of a well-structured data warehouse, ensuring data is stored efficiently and can be accessed quickly for analysis and reporting.

Types of Data Models

• Conceptual Data Model: Focuses on the high-level design, outlining the core entities and their relationships. Ideal for communicating data requirements to stakeholders.
• Logical Data Model: Provides more detailed views of data relationships, data types, and rules but remains independent of technology. Often used for database design.
• Physical Data Model: Defines how the model will be implemented in a specific database system, including table structures, columns, and constraints.

Types of Data Models in DBMS

Database Management Systems (DBMS) has the responsibility of sorting data before storing or accessing it. Thus, data models are required to keep these databases organized so that they can be used effectively and efficiently by organisations.

• ‍Relational Data Model: This model is one of the very well known data models which organizes everything in a row and columns with keys(UI-NON UI) relationships.‍
• Hierarchical Data Model: This model represents data in a tree-like structure having one/single root node and each parent can have multiple children.‍
• Network Data Model: This model is a modified version of the hierarchical model where data are stored in tree-like structures but this contains records with many-to-many relationships throughout.‍
• Object-Oriented Data Model: This is an extension of the relational model, by incorporating object-oriented programming concepts that allows for a looser schema which makes sense can excel in some modern applications.

Data Modeling Process

Stages of the Data Modeling Process

1. Requirement Gathering: To document the data required by an organization
2. Concept Design: Create a Data Architecture Conceptual View, which is an abstract representation of the data entities and how they are related to each other.
3. Logical Design: Modify the model to include data types, keys that would be used as primary and foreign key fields in a relational database, relationships between entities or even specify other integrity constraints.
4. Physical Design: How the model will be implemented in a particular DBMS.
5. Testing & Validation: Make sure the model does, what you promise to do and deliver?

Tools for Data Modeling

Today, tools make it easier to create and analyse as developers use them to begin data modeling or optimise their models. Popular Data Modeling Tools include -

• Erwin Data Modeler: Widely used for relational database modeling.
• DBT (Data Build Tool): Supports data transformation and modeling within a data warehouse.
• Power BI: Used for creating multidimensional data models and visualisations.
• TROCCO: A fully managed data platform supporting data modeling through its transformation capabilities, allowing organisations to build flexible models using SQL or Python.

Best Practices for Data Modeling

• Business Requirements: Have a clear understanding of your data requirements and business goals.
• Simplicity: Do not over complicate the model Design it simple and scalable.
• Guarantee Data Integrity: establish the relationship between entities with primary and foreign keys
• Model Renovation: Models should be maintained with timely updates as per the business necessity. Make sure to constantly reassess and fine-tune your model for anything new that arises
• Leverage modern tools: Use tools like TROCCO to automate/accelerate the data modeling process.

Conclusion

Data modeling is necessary to your data Infrastructure processes due to the tendency for us all. No matter if you are designing for a relational database, data warehouse or big-data platform, knowing the different types of Data models, and what tools to use and best practices can help us achieve a success full design.

TROCCO delivers scalable and adaptive data modeling for structuring, transforming & integrating of any kind or form of the dataset to harness complete potential out-of-the-box from your available data. Click here to get a free trial.

‍