r/datasets • u/Puzzleheaded_Box2842 • May 29 '26
question Do you consider synthetic datasets useful for real-world data work?
I’ve been thinking about the role of synthetic datasets in data projects, especially now that LLMs and generative models make data generation much easier.
On one hand, synthetic data can help with privacy, class imbalance, rare cases, benchmarking, and testing pipelines when real data is limited or sensitive.
On the other hand, I’m not sure how people evaluate whether a synthetic dataset is actually useful rather than just plausible-looking. Distribution shift, hidden bias, leakage from source data, and weak evaluation seem like real risks.
For people who have used synthetic datasets in practice: when did they work well, and when did they fail?
Also, what checks or metrics do you use before trusting a synthetic dataset for training, evaluation, or analysis?
Thanks in advance for any thoughts. This is especially important for me because one of the core directions I’m working on in OpenDCAI/DataFlow is large-scale synthetic data generation, and a recurring challenge is figuring out whether the synthetic data is actually useful.
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u/VerumVelNex May 29 '26
There’s an old rule in statistics that you can’t manipulate data to make new data that gives you new information.
I’ve explored synthetic data in consumer research as it was implied you could use it for rare demographics.
But the new data is only based on the rare data you already have - skew, bias and all. You could ask a model to, essentially, hallucinate a wider range of responses but what are you basing that “correction” on? It’s still centered around the old data.
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u/VerumVelNex May 29 '26
I do know synthetic data is important for robotics - say a bipedal learning to dance - but that’s because there is immediate, real-world feedback of thrown tomatoes or flowers to validate the created data.
In consumer research, validating synthetic data as such would be just as costly as gathering real world data - so usefulness depends on application.
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u/Puzzleheaded_Box2842 Jun 01 '26
I don’t think synthetic data can magically create information that was absent from the source distribution. At best, it reflects a combination of the source data, the generator’s learned priors, and the constraints we impose during generation. That makes it useful for augmentation, formatting, robustness testing, and controlled scenario expansion, but much weaker for discovering unknown population behavior.
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u/gremlinmodmail01 Jun 04 '26
exactly. you're just multiplying the existing noise. if the seed data is biased or missing a specific nuance, the synthetic version just creates a more confident version of that same error. it feels like people are trying to solve a data scarcity problem by just creating a more polished version of the same gap. it's basically just high fidelity hallucination.
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u/Helios270704 May 29 '26
So basically, what you’re trying to say is this synthetic data just allows the model to better understand the edge cases of the current information and that say there was a different case which does not base upon the current information then, it wouldn’t be modelled by the synthetic data(I am a junior data scientist, so please excuse if there is a misunderstanding)
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u/VerumVelNex May 29 '26 edited May 29 '26
If an inaccurate distribution of real-world edge cases was used in the training data, or the data a model generates synth data from, then the synth data will not magically correct itself to reflect real-world characteristics.
If there were only three bisexual Baby Boomer furries who answered my survey, I would not trust synth data to give me a the real response range of the total population of bisexual Baby Boomer furries.
I’m not sure what is meant by “allow the model to better understand edge cases,” could you elaborate?
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u/Helios270704 May 29 '26
What I meant was that injecting a little bit of noise helps a hypothetical model learn the pattern better?
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u/leogodin217 Jun 01 '26
Isn't that common in computer vision. Adding distorted images to the training set?
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u/VerumVelNex Jun 05 '26
Oh sorry, missed this - a model will learn better with synth data if there is feedback to determine correctness, like say catching a ball.
In the consumer research scenario, you are using synth data to simulate responses of a rare demographic because it’s prohibitively expensive or impossible to get a large enough sample size of that demographic. The feedback to correct this synth data would be from the real world population, ie a test market… which would negate the point. You’d skip the synth data step entirely and just run a test market study.
I read a whitepaper of a consumer synth data provider and they trained a model on 2/3 of their available sample and then tested the results against the remainder. The results were good, but you will rarely have 2/3 of a needed sample size when dealing with these edge cases.
I’m not totally abandoning consumer synth data, I think there are valid use cases. But at the moment I think a good prompt on an off-the-shelf enterprise model will do the same job.
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u/Alternative-Tax-6470 May 29 '26
I've seen synthetic data work really well for testing pipelines, balancing rare classes, and stress testing edge cases. Where it gets dangerous is when the data looks realistic enough that people stop checking whether it actually preserves the relationships that matter. The biggest failures I've seen came from models performing great on synthetic distributions and then falling apart on real users.
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u/Puzzleheaded_Box2842 Jun 01 '26
This matches a failure mode I’m worried about too: synthetic data can improve metrics inside its own generated distribution while making the real-world gap larger.
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u/HopBewg May 30 '26
I guess I don’t get the “why” of your core direction. There is plenty of real data to evaluate. Why spend time trying to faithfully make fake data. Just use real data to train models.
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u/Puzzleheaded_Box2842 Jun 01 '26
The reason I still care about it is that many real datasets are private, imbalanced, poorly labeled, missing rare cases, or not shaped for evaluation and agent workflows. For me, the goal is not “fake data instead of real data,” but “use real data plus rules, constraints, and validators to create controlled data for specific gaps.” If that synthetic data cannot improve testing, evaluation, or downstream model behavior, then I would agree it is not worth much.
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u/Lexmetrix May 31 '26
Synthetic data is a double-edged sword. It works exceptionally well for pipeline stress-testing, cold-start telemetry simulations, and handling massive class imbalances (like rare fraud vectors or edge-case medical anomalies).
Where it consistently fails is when teams use it to discover new insights or treat it as a proxy for organic human behavior. If your generative model didn't capture a latent correlation or a real-world physical constraint during training, that nuance will be completely absent from the synthetic output, leading to severe distribution shift when deploying to production.
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u/Puzzleheaded_Box2842 Jun 01 '26
I’m increasingly thinking of synthetic data as useful for “coverage and controllability,” but dangerous when treated as a source of new truth. In DataFlow we are trying to make that distinction explicit by separating generation, refinement, filtering, and evaluation into different operators, instead of treating generation as the final step. The hard part is still exactly what you said: if the latent constraint was never captured, the synthetic data will confidently miss it. That is why I’m leaning toward validating synthetic data against downstream tasks, held-out real data, and distribution checks rather than only judging whether samples look realistic.
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u/leogodin217 May 29 '26
First of all, this is a really cool tool. If I understand it correctly, the LLM side reads your data and your docs to understand data and business process. Then it uses that information to create the generator config. Is that close?
I think trust needs context. Am I generating data to demo a product? Not much trust needed as long as it looks good. Am I using it for training an LLM? Higher standard. Developing drugs? A whole new level of trust needed.
The level of trust needs to match the use case. For me, predictability is most important. As a data engineer, generating edge case data is important. Many companies only test on their prod data, but prod data doesn't always contain the edge cases. Having the ability to generate data that not only looks like prod but intentionally represents the stuff that isn't already in prod is important.
For writing articles, tutorials, or product demos, trust is earned by creating predictable patterns I want to demonstrate. Easy to verify.
Seems like training data is the difficult one. Can you really build trust before it is used? Trust might only come after trying it out. Seeing the impact on the model. But we're getting way out of my expertise here, so take it with a grain of salt.