Synthetic data

What is synthetic data?

Synthetic data is artificial data generated to look and behave like real data, used for demos, testing and training without exposing real records. A model learns the patterns in a real dataset, such as deal sizes, stages and renewal dates, then produces new records with similar patterns. NIST and the U.S. Census Bureau describe it as a way to share useful data while protecting the people in it.

Why it matters to a revenue leader

AI sales tools are often demonstrated on synthetic data, and clean made-up data makes any product look sharp. Your CRM has duplicate accounts, missing close dates and branch-by-branch naming habits. Synthetic data has a fair place in testing, especially when you cannot share customer records with a vendor during an evaluation. It tells you little about how a tool will perform on your own pipeline, so the real test has to happen on your data, under your data rules.

An environmental services company with 22 sellers evaluates two AI pipeline tools. Both look strong on the vendor's synthetic dataset of 5,000 opportunities. When each tool runs on a de-identified export of the company's own 1,400 open opportunities, one flags 60 stalled deals that managers agree are stalled, and the other flags 15. The demo data could never have shown that gap.