What is Gretel?

Gretel is a cloud-based platform that helps companies create “synthetic” data - fake but realistic data that mimics real information without exposing any private details. It uses artificial intelligence to generate this data safely and quickly.

Let's break it down

  • Cloud-based platform: a service you access over the internet, no need to install software on your own computer.
  • Synthetic data: data that looks like real data (same patterns, formats) but is artificially created, so it doesn’t contain actual personal or confidential information.
  • Mimics real information: the fake data keeps the same statistical relationships as the original, so it can be used for testing, training models, etc.
  • AI-generated: machine-learning algorithms analyze the real data and then produce new, similar data automatically.
  • Safely and quickly: because the data is synthetic, privacy risks are reduced, and the process is faster than manually cleaning or anonymizing data.

Why does it matter?

Using real data can expose personal details, violate regulations, or cause security breaches. Synthetic data lets businesses develop and test software, train AI models, and share data with partners while staying compliant with privacy laws like GDPR or HIPAA.

Where is it used?

  • Software testing: developers use synthetic data to test new features without risking real user information.
  • Machine-learning model training: data scientists train algorithms on synthetic datasets when real data is scarce or too sensitive.
  • Data sharing between companies: partners can exchange realistic data for collaboration without exposing actual customer records.
  • Regulatory compliance audits: organizations generate synthetic logs to demonstrate processes without revealing confidential logs.

Good things about it

  • Protects privacy and reduces legal risk.
  • Speeds up data preparation compared to manual anonymization.
  • Keeps statistical properties, so models trained on synthetic data perform similarly to those trained on real data.
  • Scalable: can generate large volumes of data on demand.
  • Enables safe data sharing across departments or organizations.

Not-so-good things

  • Synthetic data may miss rare edge cases that exist in real data, potentially lowering model accuracy.
  • Quality depends on the original dataset; biased source data can produce biased synthetic data.
  • Some industries (e.g., finance) may require proof that synthetic data truly represents real-world behavior, adding extra validation steps.
  • Costs can add up for high-volume or highly complex data generation if using premium cloud services.