What are the main use cases for synthetic data, and what do they have in common?

Sharing data before access is granted, developing/testing code, reproducible open science, and teaching — all let you work with realistic data without ever touching real personal records.

The unifying theme is getting useful, realistic data into people's hands without exposing real individuals — synthetic data acts as a stand-in wherever the real data is too sensitive, too slow to obtain, or too risky to share. Four common cases:

• Intermediate data sharing — hand recipients a preliminary synthetic dataset while they figure out their exact needs or wait out a lengthy approval process, so they can explore and plan before anyone unlocks the real sensitive data.
• Code development — developers write and test against synthetic records (matching the real schema, column names, and types) without ever pulling real personal data into a dev environment.
• Open science workflow — researchers publish a synthetic dataset alongside their code so others can reproduce the analysis and check the methods without accessing the protected original.
• Educational purposes — teaching and training on realistic data, dodging the ethical and legal complications of putting real people's records in front of students.

Tip: Notice the pattern: synthetic data shines as a substitute that unblocks work — exploration, testing, reproduction, teaching — long before (or instead of) anyone touches the sensitive original.