What is synthetic data?
Synthetic data is generated by a model that learns the patterns in a real dataset, such as how age, region, and spend relate, then produces new records that follow those patterns. No row matches a real person, but the whole dataset behaves like the real one.
In marketing, the term also covers synthetic respondents, AI-generated answers that stand in for survey participants. That use is more contested, because the answers come from a model's general knowledge rather than from the brand's real customers.
Where does synthetic data help, and where does it mislead?
| Use | Why it helps | Watch out for |
|---|---|---|
| Testing systems | Realistic data without exposing customer records | Edge cases the generator never produced |
| Rare cases | More examples of fraud, outages, or unusual orders | Invented patterns that don't occur in reality |
| Sharing data | Partners can work with data that isn't personal | Records that still reveal real people if generated carelessly |
| Synthetic respondents | Fast, cheap early reads on concepts | Answers that reflect a model's assumptions about customers |
Privacy depends on how it's generated. A generator that memorizes rare records can leak them, which is why careful generators add protections such as differential privacy.
What is an example of synthetic data?
Testing a pipeline before it sees real data
A team builds a new reporting pipeline for a retailer but can't use real order data until legal review is complete.
It generates synthetic orders that match the real mix of regions, products, returns, and refunds, including a few broken records on purpose. The pipeline is tested and fixed before a single real customer record flows through it.
Synthetic data vs. anonymized data vs. real data
| Real data | Anonymized data | Synthetic data | |
|---|---|---|---|
| Source | Actual records | Actual records with identifiers removed | Generated from learned patterns |
| Privacy risk | Highest | Lower, though records can sometimes be re-identified | Lowest, if generated with care |
| Trust for decisions | Highest | High | Only after checking against real data |
Why does synthetic data matter?
It solves real problems in testing, privacy, and scarcity. It also tempts teams to skip the harder work of learning from real customers.
- Use it to build. Testing pipelines, agents, and dashboards is where it shines.
- Be careful using it to discover. It can't reveal a customer insight the source data didn't already contain.
- Validate it. Compare its distributions and key relationships with the real data before relying on it.
How does Alli do this?
Alli by PMG's Audience Planner builds Live Personas from billions of real behaviors across streaming, social, commerce, creators, and professional networks, combined with a brand's first-party insights, and keeps them current as behavior shifts.
Teams can talk to a published persona through Ask Alli to test ideas before spending, and because the persona is grounded in behavioral signals, its answers reflect real patterns in the audience rather than a model's general guesses.
Explore Alli Audience PlannerSynthetic data FAQ
What is synthetic data used for?
Testing software and pipelines, training models when real examples are scarce, creating more examples of rare events, and sharing data without exposing real people.
Is synthetic data private?
It can be, but not automatically. A generator can memorize and reproduce rare real records, so privacy depends on how the data is generated and checked.
Can synthetic data replace real customer research?
Not for discovering new insights. Synthetic data and synthetic respondents reflect the data or model they came from, so they can't reveal what real customers think that the source didn't already capture.
What is the difference between synthetic data and AI personas?
Synthetic data is artificial records for testing or privacy. AI personas are audience profiles people can question. A persona built from real behavioral signals isn't synthetic data, even though AI gives it a voice.
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