pre-dev-data-model

Data modeling starts with abstract entity and relationship definition independent of database technology. Define entities, their attributes with types, constraints, and how they connect (one-to-one, one-to-many, many-to-many) to capture functional requirements while deferring implementation choices.

Conceptual data modeling captures entities, relationships, and ownership at an abstract level without database-specific constructs like foreign keys or table syntax. Schema design follows later, translating that model into a chosen database technology's specific structure.

Explicitly map which component owns which entity or data set during modeling. Clear ownership prevents anti-patterns like shared databases and ensures consistent data governance, lifecycle rules, and access patterns before technology selection.

Apply data modeling to initiatives with persistent data and interrelated entities where schema conflicts and migration risks are high. Early conceptual work guides data analysis, lifecycle rules, and access patterns, avoiding costly redesigns later.

Yes. Data modeling remains technology-agnostic by design, using abstract relationships and entity definitions. This approach lets you analyze requirements, model ownership, and validate structure before selecting a database platform.

Capture entities, attributes with types, constraints, relationships between entities, data quality rules, lifecycle requirements, data ownership boundaries, and access patterns. This complete specification satisfies functional requirements while remaining independent of implementation.