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Protecting your customers begins with best practices for securely capturing, storing, and protecting the data you collect for or about them. When an organization has a large enough dataset, needs typically arise for doing analytical workloads or training machine learning models on this data. If you use random or mock data to generate a report or train a model, you arrive at an output that doesn’t reflect the true use case of the organization. Success on tasks like this seems to require production data.
Alternatively, perhaps production-like data is good enough. In this episode, I interview Alex Watson, co-founder and chief product officer at gretel. We discuss their solution for privacy preserving synthetic data that remains representative of the underlying dataset.
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