Synthetic Data Is Becoming an AI Engineering Requirement
Most data and Artificial Intelligence (AI) teams spent 2025 trying to feed larger models without copying production records into yet another sandbox. Privacy programs blocked the old workaround of cloning a warehouse and hashing a few columns, while product teams still needed volume, rare events, and shareable sets for vendors. Capital followed that bind: synthetic data generation startups raised more than $145 million in 2025, the highest annual total Tracxn records for the category in a decade of tracking, with more already booked in 2026. The pressure shows up in procurement as a line item next to test environments, model training, and third-party sharing, not as a research talking point.
What “Synthetic” Actually Means in a Dataset
The National Institute of Standards and Technology (NIST) describes synthetic data generation as a process in which seed data are used to create artificial data that have some of the statistical characteristics...
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