Synthetic data
What is Synthetic data?
Artificially generated data that mimics the patterns of real data without being real records. Teams use it to train and test AI when real data is scarce, sensitive or unbalanced, for example to protect personal information or to cover rare cases. The catch: if the generator is flawed, the synthetic data inherits those flaws and can amplify them.
Why it matters
Synthetic data is most attractive exactly where real data is hardest to get: locked behind privacy rules, too rare to collect, or too skewed to train on safely. The awkward part is validation. To trust that generated records behave like the real thing, you have to compare them against real data you may not fully have, which is the shortage you were working around in the first place. Privacy needs care too. Synthetic does not automatically mean anonymous; if the generator sticks too close to its source, real individuals can sometimes be traced back out of supposedly artificial records. Used well it unblocks projects that data access would otherwise stall. Used carelessly it launders a false sense of both quality and safety.
In practice
A bank wants to train a fraud model but cannot freely share real transactions across teams. It generates synthetic transactions that preserve the statistical patterns without exposing any customer, then validates them against a held-back slice of real data before trusting the result. Only once the synthetic set behaves like the real one on the checks that matter does it become the shared training data. The validation step is what makes it safe to use.
