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What is Synthetic Data?

Synthetic Data is obtained by generating artificial data that incorporates an original dataset's statistical properties and distributions, thus reflecting real-world data. This data augmentation technique can be used instead of or in addition to original data to improve AI and Analytics projects and to solve different data-related problems.
"Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models."
Gartner

Why Synthetic Data?

As companies start to accelerate their AI adoption within their business processes, they face escalating challenges as they take stock of the data required for the AI models.
These issues are often related to data governance aspects like access and sharing of privacy sensitive data and related data retention problems or sometimes data quality is not good enough to guarantee a successful outcome. Compared to other anonymization techniques, or pseudonymised data, synthetic generation joins utility and privacy goals. Its risk of reidentification is very low and it reduces AI projects costs related to data collection and labeling.
Protect data and preserve its privacy
Synthetic generation improves de-identification and creates data sandboxes to share data inside and outside your organisation easily.
Augment Data for ML & LLM Success
Unlock AI’s potential: use synthetic data to enrich ML datasets, train LLMs, and fine-tune AI models with diverse, high-quality data.
Take a step forward towards AI fairness
Synthetization is helpful to fix possible bias that lies within the data and ensure a more inclusive AI application.
Improve and automate software testing
Synthetic Data lays the foundation for safer and better testing, for example in data migration cases or as test data generation.
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