Synthetic data – algorithmically generated data that mimics real-world data – has emerged as a cornerstone in modern AI workflows.
But its promise comes with persistent myths about its capabilities, limitations and reliability. Synthetic data is being explored across industries, from training machine learning models to helping businesses safeguard customer privacy.
And with that growth comes confusion.
If you’ve been in spaces where someone waved off synthetic data as “fake” or “unreliable,” you’re not alone. The hype, the fear and the technical jargon often cloud what synthetic data actually is – and what it isn’t.
Let’s unpack five myths surrounding synthetic data and clarify what’s really true.
Myth #1: Synthetic data is low-quality data
Reality: When done right, synthetic data can be incredibly high-quality and, in some cases, even better than real-world data.
Let’s be clear: not all synthetic data is created equal. It will be garbage if it’s poorly generated without respect for ethics, real-world patterns or business context. However, when synthetic data is built using well-tuned models trained on solid data foundations, it can match real data in structure, variety and complexity.
In fact, synthetic data often avoids the “noise” of real-world datasets – outliers, errors, missing fields – making learning easier for algorithms. It’s datasets that mirror reality, with the flexibility to fix imbalances or simulate edge cases that real data might not capture.
Synthetic data isn’t automatically better or worse than real-world data. It is all about how it’s generated. When generated with ethical rigor, real-world fidelity, and business context in mind, synthetic data can rival or even surpass real data in quality. Vrushali Sawant, Data Scientist
Myth #2: It’s “fake” data, so it can’t be trusted
Reality: Synthetic data isn’t fake; it’s designed to reflect reality, not replicate it.
There’s a big difference between “fake” and “synthetic.” Fake implies something dishonest or deceptive. Synthetic data is deliberate: it’s built to simulate the statistical patterns of real datasets, without copying them directly.
Synthetic data preserves statistical integrity; fake data preserves the imagination of whoever made it up. David Weik, Sr. Software Development Engineer in Test
Think of it like a digital twin – not a clone of a specific person’s data, but a reflection of overall behavior and trends. That’s the whole point: to generate data that behaves like the real thing, while breaking the connection to any specific individual or event.
This is especially useful in scenarios where real data is too sensitive to share or too sparse to use effectively. With synthetic data, teams can still develop, test and iterate – without waiting for perfect conditions.
Myth #3: Synthetic data isn’t safe because it still leaks private information
Reality: When generated responsibly, synthetic data is among the most privacy-preserving tools.
Indeed, anonymizing real data doesn’t always make it private, especially in a world where cross-referencing data is easy. But synthetic data offers a clean break. Since it isn’t derived row-by-row from real people, it doesn’t carry direct traces of individuals.
Good synthetic data practices go further by intentionally preventing re-identification. That means generating data that looks and behaves like the real thing but doesn’t match anyone’s records. It’s a different mindset: privacy by design, not by deletion.
Leveraging differential privacy in synthetic data generation means privacy by design, not by accident. David Weik
This makes synthetic data ideal for industries like health care, finance and education, where data access is tightly regulated but innovation still needs to move fast.
Myth #4: Models trained on synthetic data won’t perform in the real world
Reality: In many use cases, models trained on synthetic data can match or even outperform models trained on real data.
Questioning whether “imaginary” data can teach a model real-world behaviors is natural. But synthetic data isn’t imaginary. It’s purpose-built to reflect the patterns that matter. And when used correctly, it can be a game-changer for training machine learning models, especially in edge cases or underrepresented categories.
The key is understanding when to use synthetic data alone and when to mix it with real data. A hybrid approach works best in many high-performing systems: use synthetic data to expand, balance, or pre-train and real data to fine-tune.
What’s important isn’t just the data type – it’s whether the data matches the problem you’re solving.
Myth #5: You can plug it in and go
Reality: Like any powerful tool, synthetic data requires ethical consideration, structure and ongoing care.
One of the biggest misconceptions about synthetic data is that it’s a shortcut. Just hit “generate” and your problems are solved. SAS CTO Bryan Harris said similar about generative AI in that it is not a one-size-fits-all, one-time, or one-button solution for business problems.
In practice, synthetic data is part of a system – it needs the right inputs, modeling choices and a clear sense of what it’s meant to support.
Synthetic data isn’t a shortcut; it is a responsibility. To be valuable, it must be ethically designed, privacy-aware and continuously monitored to prevent bias and preserve trust. Vrushali Sawant
Creating synthetic data isn’t the end of the process; it’s the beginning. Teams need to validate that it performs well, monitor for drift over time and ensure it doesn’t introduce new biases or inaccuracies. That’s not a knock – it’s the reality of working with any data product.
When synthetic data is treated as an asset, not a gimmick, it delivers value. But it has to be designed, integrated and maintained with the same rigor as any other system.
Synthetic data isn’t a myth
Synthetic data is not a buzzword. It’s a practical, evolving solution to some of the toughest organizational challenges. But its value depends on how we approach it, not as a magic fix but as a tool that requires precision and intent.
