Synthetic data is rapidly gaining traction, but evaluating its quality remains complex. What works for one application may not be suitable for another. Given its critical role in training machine learning models, testing applications, and ensuring data privacy, it’s essential to assess how well synthetic data mirrors real-world data while safeguarding sensitive information. DevOps and data teams must prioritize the right metrics in testing environments. In this session, we’ll provide practical insights into assessing and applying synthetic data effectively, helping attendees understand its limitations and key considerations for different use cases.