Data models play an integral role in the development of effective data architecture for modern businesses. They are key to the conceptualization, planning, and building of an integrated data repository that drives advanced analytics and BI. In this blog post, we’ll provide you with an overview of the most popular data modeling techniques and best practices to ensure an agile and efficient data warehouse development process. 

The proven approach to seamlessly designing and deploying a data warehouse is putting enterprise data modeling at the center of your data warehousing process. By doing so, you can ensure a seamless path from design to development and deployment.

Though data modelers have multiple approaches to creating these schemas, it’s critical to pick the right one for your business use case. 

What Is Data Modeling?

Data modeling is the process of designing a framework that defines the data relationships within a database or a data warehouse. It involves creating a visual schema to describe associations and constraints between datasets. It gives a conceptual representation of data and visualizes the interrelation between datasets within a system. There are three main perspectives of data models: 

• Conceptual Model: It is a visual representation of database concepts and focuses on determining the entities within a system, their characteristics, and relationships between them. 

• Logical Model: It defines the structure of the entities and provides further context on their relationships, providing a technical map of data structures and rules.  

• Physical Model: It is a framework or schema specifying how the model will be built in a database. It represents the tables, columns, data types, etc. 

Data Modeling Techniques

The following are a few data modeling techniques to know:

Network Technique

A network technique involves designing a flexible database model representing objects and their relationships. It has a schema that provides the logical view of the database in a graphical form. The network model is similar to a hierarchical model, but unlike the latter, it supports multiple parent and child records, making it easier to handle complex relationships.

Entity-Relationship Modeling

Entity-relationship modeling is a technique used to define data elements, entities, and their relationships in a database. This technique involves creating an entity-relationship diagram comprising entities, attributes, and relationships in a graphical format. It serves as a conceptual blueprint to be implemented as a database.

Relational Technique

Relational technique is used to describe the relationships between the data elements stored in rows and columns. These relations between the entities can be one to one, one to many, many to one, and many to many. Data modelers use this technique to minimize the complexity and ensure a clear overview of the data.

Data Modeling Best Practices

• Look at the business process from the most holistic sense possible so you can identify all the component systems and entities relevant to your use case. 

• When using 3rd Normal Form, create narrow tables for your datasets. Wide tables result in longer scans/reads, which can affect performance of your data model – especially when dealing with large volumes of data or multiple tables.

• Always build a data model around a business process. This approach will make it easier for analysts to navigate the data model and quickly get the answers to their questions.

• Evaluate data on a granular level. The more you know about each dataset, the more appropriately you can place it into the data models.

Conclusion

Data modeling is about understanding your business and data before moving forward with analytics. Equipping yourself with the knowledge of modern data modeling techniques and best practices will help you build a data model that will serve your business and end-user requirements. 

Moreover, using automated tools to optimize the data modeling process is a great way to fast-track data management projects. Modern solutions eliminate the need to manually code, configure, and test your schema, significantly cutting down the time to design a data model. 

As a result, you can quickly set up a functional data warehouse and start your analytics journey.