The lack of access to production data presents significant challenges for software development teams. Production data is an enterprise's most valuable asset as it contains organizational secrets, employee and customer information, and other sensitive data that carries substantial privacy risks if distributed across multiple environments or granted broad user access. In an attempt to maintain privacy, organizations often resort to imperfect solutions, such as randomized data generators, weak de-identification using manual scripts, or a combination of custom statistical steps. These solutions cause delays in development, fail to guarantee privacy across environments, and compromise the reliability of applications.
In this webinar, Gretel Senior Engineering Manager Ilgin Luks, Senior Software Engineer Mike Knepper, and Product Manager Grace King explore the role of synthetic data in providing safe access to production-grade data for testing and development.
Learn how synthetic data can:
- accelerate safe access to sensitive data in lower environments through anonymization
- promote realistic unit testing with high-quality synthetic data
- unlock scenario-based feature testing and evaluation with conditional generation
- enable load and stress testing by boosting small datasets
- reduce the time and cost of development cycles with data subsetting
