Synthetic Data: Promise, Pitfalls, and How to Get Data Quality Right

The data landscape is shifting from observation – learning from historical signals – to generation, where models are used to predict, simulate, and augment reality. This shift is being driven by growing data scarcity, stricter privacy requirements, and the limitations of traditional data collection. 

Synthetic data offers enormous promise, but only when quality and validation are built in from the start. While generating synthetic data has become relatively easy, ensuring it is accurate, reliable, and fit for decision-making remains the real challenge. 

In this session, we’ll share how synthetic data is being applied today and, more importantly, how to evaluate its quality before it’s put into production. You’ll walk away with practical frameworks for validating synthetic data and avoiding the risks that come with using unverified or poorly tested models. 

You’ll learn: 

• Why the industry is moving toward synthetic data—and what’s driving adoption now 
• Where synthetic data delivers real value vs. where weak validation can introduce risk
• How to assess and apply synthetic data with confidence using quality-first frameworks