Best practices for data modeling
A well-designed Data Model provides a strong foundation for building scalable, reusable, and maintainable applications. Applying data modeling best practices early reduces rework, avoids duplication, and ensures data can be consistently reused and extended as business requirements evolve.
These best practices apply whether you design a Data Model from scratch, extend an existing solution, or base it on an Industry Foundation Data Model.
Design principles for scalable Data Models
Apply the following practices when you design data models in Pega Platform:
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Model data as reusable data entities: Model business data as reusable data entities instead of tying it to individual Cases. Sharing the same data across Cases and applications improves consistency and reduces redesign of the Data Model as solutions grow.
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Reuse existing data and avoid duplication: Define each piece of data in a single place. Reuse existing data entities when possible, and extend them only when you need additional information. Reuse maintains a consistent view of business data and lowers long-term maintenance effort.
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Select the right level of responsibility for data: Define data entities by what the information represents, separate from how a specific Case or Process uses it. Data focused on business meaning is easier to reuse, understand, and adapt.
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Design data at the appropriate scope: Place data that you share across multiple solutions at a broader scope, and keep solution-specific data within the application that uses it. This separation supports reuse and lets individual applications evolve independently.
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Separate business data from reference data: Identify data that rarely changes, such as lists or codes, and manage it separately from transactional business data. Separating reference data simplifies updates and promotes reuse across the application.
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Keep data design independent of business logic: Design the Data Model to represent the structure of the information, separate from how processing accesses or uses the data. This separation lets business logic and data sources change over time while the Data Model remains stable.
By modeling data as reusable entities, defining each piece of data once, designing at the appropriate scope, and separating business logic from data access, you create scalable, reusable, and maintainable applications. Following these practices reduces refactoring and helps your Data Models adapt as requirements and systems of record evolve.
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