Alex Watson: Synthetic Data Deep Dive

Alex discusses his background working at the NSA and then starting, which was acquired by and integrated into AWS. He then goes into his latest venture,, which provides tools for creating anonymized and synthetic datasets.

Guest Bio

Alex Watson is the co-founder and CEO of, a startup that offers APIs for creating anonymized and synthetic datasets. Previously he was the founder of, whose product Macie, an analytics platform protecting against data breaches, was acquired by AWS.

Links Mentioned

Show Notes

02:15 Introducing Alex Watson

03:45 How Alex was first exposed to programming

05:00 Alex's experience starting Harvest AI, getting acquired by AWS, and integrating their product at massive scale

21:20 How Alex first saw the opportunity for

24:20 The most exciting use-cases for synthetic data

28:55 Theoretical guarantees of anonymized data with differential privacy

36:40 Combining pre-training with synthetic data

38:40 When to anonymize data and when to synthesize it

41:25 How Gretel's synthetic data engine works

44:50 Requirements of a dataset to create a synthetic version

49:25 Augmenting datasets with synthetic examples to address representation bias

52:45 How Alex recommends teams get started with

59:00 Expected accuracy loss from training models on synthetic data

01:03:15 Biggest surprises from building

01:05:25 Organizational patterns for protecting sensitive data

01:07:40 Alex's vision for Gretel's data catalog

01:11:15 Rapid fire questions