Salesforce Architect Secrets – Data Model design considerations

In this article, I would like to explore the common mistakes made in designing the data model for a Salesforce application that can hamper scalability.

When designing the data model for a Salesforce application, it is important to consider several factors such as the expected volume of data, the complexity of the business processes, and the data access patterns. Failure to take these factors into consideration can lead to a poorly designed data model that is difficult to scale, maintain, and extend.

One common mistake made in designing a data model for a Salesforce application is to create a large number of custom fields and objects without considering the impact on system performance. This can lead to a bloated data model with a high degree of complexity that is difficult to maintain and scale.

Another mistake is to create a flat data model without taking into account the relationships between different entities. This can lead to data redundancy, inconsistencies, and poor data quality.

To avoid these issues, it is important to adopt a model-view-controller (MVC) approach to data modeling in Salesforce. In this approach, the data model is designed with the following principles in mind:

• Modularity: The data model should be modular, with well-defined entities and relationships between them. This makes it easier to maintain and extend the data model over time.
• Normalization: The data model should be normalized, with a focus on eliminating data redundancy and improving data quality. This ensures that data is consistent and accurate across the application.
• Separation of concerns: The data model should be separated from the business logic and the user interface. This ensures that changes to the data model do not impact the rest of the application, and vice versa.
• Scalability: The data model should be designed with scalability in mind, with a focus on minimizing the number of custom fields and objects, and maximizing the use of standard Salesforce functionality. This ensures that the application can scale to handle large volumes of data and complex business processes.

In conclusion, designing a data model for a Salesforce application requires careful consideration of several factors, including the expected volume of data, the complexity of the business processes, and the data access patterns. Failure to take these factors into account can lead to a poorly designed data model that is difficult to scale, maintain, and extend. By adopting an MVC approach to data modeling in Salesforce, organizations can create a scalable and maintainable data model that meets the needs of their business processes.