How are you handling training data when public datasets don't match your use case? [D]

Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize properly. Collecting real-world proprietary data takes months. What do people actually do? From what I have seen, the options tend to be:

- Ship with what you have and accept degraded performance
- Spend weeks scraping and cleaning, which eats engineering time
- Augmentation techniques like SMOTE or noise injection, which help at the margins but do not solve domain specificity

I am working on a project that approaches this differently. Sourcing permissively licensed real-world data, curating it to a company's specified schema, then running synthetic expansion to hit the volume and edge case coverage the model actually needs. Every output includes a fidelity report showing statistical alignment between the synthetic output and the source distribution.

Before going further with it, I genuinely want to know whether this is a pain people feel acutely or whether most teams have found workarounds that make something like this unnecessary.

If you are hitting a data wall on something you are building right now, I would love to hear what the specific bottleneck looks like.

What has worked for you?