In the previous blog, we discussed the design strategy for architecting a data model that aligns with business needs, leverages Salesforce-native capabilities, and remains scalable as the system grows. A key part of this strategy is first aligning business entities conceptually and translating them into a logical blueprint. Only after the logical model is clearly defined should those entities be mapped to Salesforce constructs such as core data models for Sales and Service, s

In the previous blog, we explored data categorization — a foundational concept that must be mastered before moving into data model design. Categorizing data correctly shapes every architectural decision that follows. Designing a data model that aligns with business needs, leverages Salesforce-native capabilities, and remains scalable over time depends heavily on understanding these core design principles. As a Salesforce architect, your responsibility goes beyond configuring

Most business processes are fundamentally data-driven. Every interaction, transaction, approval, integration, and automation either generates, transforms, or consumes data. As a Salesforce architect, understanding the types of data being captured and created throughout a process  is critical to designing a solution that is scalable, performant, and aligned with platform constraints. Data categorization is not just a modeling exercise — it is an architectural decision framewor