The Trade-Offs of Privacy

Advances in ML have transformed data privacy from a regulatory necessity into an opportunity to improve the work of data people.

Synthetic data for modeling + testing is one example of a hard thing that's now easy - and in this conversation with Tristan and Julia, Ian + Abhishek cover many other ways that privacy can actually be a skill that propels your work forward, rather than a mere legal best practice.

For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. 

The Analytics Engineering Podcast is sponsored by dbt Labs.