Synthetic data

What it is

IBM defines synthetic data as artificial data designed to mimic real-world data. It’s generated with statistical methods or AI techniques, including generative models, rather than collected from real events. IBM notes growing use in sectors such as finance and healthcare, where real data is scarce or hard to access for privacy reasons.

How it works

1. Study the structure and statistics of a real dataset, or describe the data you need.
2. Generate new records with similar properties.
3. Validate that the synthetic set behaves like the real one for your purpose.

A small-business example: ask an AI model to write 30 realistic but fictional customer emails, including edge cases such as angry, vague or off-topic messages. Use them to test a support triage workflow before it touches real inboxes.

Benefits

• Test automations and evals without using real client data.
• Fill gaps for rare cases.
• Easier to share with vendors.

Limits and risks

• Poorly generated data can mislead tests and models.
• Some methods can leak details of the original data, so treat data generated from real records as sensitive.
• It’s not a substitute for checking results on real data before launch.
• We found no vendor-neutral official guide on generating test data, so this page relies on IBM’s overview.