Synthetic data, defined
Synthetic data is information created by an algorithm instead of collected from real events. A generator learns or is told the shape of real data (field types, ranges, correlations, language style) and then produces new rows, documents or conversations that follow the same pattern.
The output looks like the real thing but does not describe actual customers, transactions or employees. That is the appeal: teams can test software, share a dataset across a boundary or pad out a thin training set without moving the original records.
How is synthetic data generated?
There are three common routes, and many pipelines mix them.
| Method | How it works | Typical use |
|---|---|---|
| Rule-based | Engineers write constraints and templates that emit records | Software testing, form and schema checks |
| Simulation | A model of a process (traffic, a warehouse, a call queue) runs and logs what happens | Robotics, logistics, operations planning |
| Model-generated | A trained model writes new text, tables or images from prompts or learned patterns | Expanding training sets, rare-case coverage |
Each route needs a reference. A rule set reflects what its authors already know. A simulation reflects the assumptions built into it. A generative model reflects whatever it was trained on.
Synthetic data vs real data
Neither is better in every case; they do different jobs.
| Question | Real records | Synthetic records |
|---|---|---|
| Where do they come from? | Actual operations, customers and staff | A generator or simulation |
| Do they contain real exceptions and mess? | Yes: odd approvals, half-finished tasks, workarounds | Only the exceptions the generator was shown |
| Privacy exposure | Depends on redaction and rights | Lower, but not automatically zero |
| Ground truth for outcomes | Real decisions and what happened next | Assumed or modeled outcomes |
| Scarcity | Limited to what a company actually holds | Cheap to produce at volume |
A generator cannot report a surprise it was never shown. That is the core limit, and it is explored in more depth in the limits of synthetic training data.
Why synthetic pipelines still need real examples
Most serious synthetic-data work starts from real material: a seed set to teach the generator, and a held-out real set to check whether the output behaves like reality. Researchers at Epoch AI discuss synthetic data as one pathway if public human-written text becomes a constraint, alongside other approaches, and their projections carry wide uncertainty.
For business records the gap is sharper. The things AI agents must learn, such as how a multi-step workflow ends, which exceptions get escalated and what a good outcome looks like, live inside companies. Generating plausible-looking tickets is easy; generating the true history of how a real team resolved them is not.
What this means for a referral partner
You do not need to argue synthetic data away. The accurate message is that it complements real data and rarely replaces it. A company with years of connected, rights-cleared operational records holds something a generator cannot invent.
Useful screens when a business owner raises it:
- Does the company hold records of real work? Tickets with resolutions, approvals, deal histories, engineering reviews.
- Does it have rights to license them? Records about its own operations usually differ from records it holds on behalf of clients; see what a data asset is.
- Is the material mostly free text and documents? Look at what unstructured data is and why it matters here.
How rewards work
Partners earn 25% of the eligible platform fees SourceX actually collects from the referred company's licensing deals, capped at $100,000 per referred company. The reward is paid only after the buyer pays and SourceX receives its fee. An introduction, meeting or signed agreement alone does not trigger payment, and no reward is guaranteed. Details are in the program terms.
When synthetic data is not the point
Skip the synthetic-data angle if the company's records are mostly someone else's data, such as an outsourcer's client files, or if nobody can export them. The conversation should start from rights and records, not from a technology debate. Ownership questions are covered in customer data ownership clauses, and who can authorize a license settles who signs.
Next step
Run a candidate through the company fit checker, then register as a partner to make the introduction. Check who qualifies first if you are unsure about size or history.