Introduction

Synthetic Data unlocks the full potential of AI and analytics by creating entirely new data that mirrors the patterns of your original datasets. This new data contains no sensitive information. You can train and test AI models without risk. You can also scale these models without exposure or compliance violations.

Advantges of Synthetic Data over Anonymized Data:

• Preserve utility for analytics, machine learning, and testing while minimizing privacy risks.
• Can simulate rare events or edge cases in data.
• Does not have a 1:1 mapping to real records.
• Is not regulated or biased.
• Cannot be traced back to any individual.

Use Cases

Synthetic Data is used for:

• Training machine learning models without exposing sensitive data.
• Sharing data across teams or vendors while maintaining compliance.
• Replacing expensive or hard-to-source real-world data collection.
• Testing and development environments that replicate real-world complexity without privacy risks.
• Monetizing data and evaluating vendors.