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Linked data keeps database records connected to the parts of Oculon that need them. That makes the data more reusable, but it also means source changes can have wider downstream effects. Placeholder image for future Oculon screenshot showing database fields linked into models, dimensions, and boards

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Where linked data shows up

Database data may later be used in:
  • models and model inputs
  • dimension mappings
  • shared reporting views
  • boards that depend on the same underlying records
This is why a database should not be treated as isolated infrastructure once it is in active use.

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Why linked data is valuable

Linked data reduces duplicate maintenance. When the source updates cleanly:
  • the same database can support several workflows
  • teams do not need to rebuild the same dataset repeatedly
  • source-level cleanup improves several downstream outputs at once

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What breaks when the source changes

Linked workflows become fragile when the source structure changes without review. Common risks include:
  • renamed fields
  • changed category values
  • inconsistent date behavior
  • missing records after a refresh
  • structural changes that no longer match downstream mappings

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Review dependencies after change

If the database changes, review the linked workflows before you assume everything still behaves correctly. Start with:
  • the mapped fields
  • any dependent dimensions
  • downstream models
  • key boards or tables used by other teams

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Keep ownership visible

Linked data works best when the source database has a clear owner and the downstream consumers understand what they are inheriting. That reduces the chance that one team quietly changes a source structure that several other teams depend on.