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Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI
Methodology Paper No. 1482026 Edition
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Abstract
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. Grounded across academia (Argyle; Bisbee; Shumailov et al., Nature; the MIT Synthetic Data Vault), the tech industry (Gartner), and MR practice, plus the ICC/ESOMAR Code and ISO 20252.
Suggested headline & dek
Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI
A field guide to four kinds of synthetic data, where each earns its place, where it breaks.
Edit for your house style, but please keep the meaning intact (no material modification) and credit CatalystMR with a canonical link back.
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APA
CatalystMR Research Team. (2026). Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI (Methodology Paper No. 148). CatalystMR. https://www.catalystmr.com/insights/methodology-papers/real-synthetic-or-both/
BibTeX
@techreport{catalystmr_mp148,
author = {{CatalystMR Research Team}},
title = {Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI},
institution = {CatalystMR},
type = {Methodology Paper},
number = {No. 148},
year = {2026},
url = {https://www.catalystmr.com/insights/methodology-papers/real-synthetic-or-both/}
}
RIS
TY - RPRT
AU - CatalystMR Research Team
TI - Real, Synthetic, or Both: A Methodology for Sourcing Decision-Grade Data in the Age of AI
T2 - CatalystMR Methodology Papers
PB - CatalystMR
PY - 2026
M1 - No. 148
UR - https://www.catalystmr.com/insights/methodology-papers/real-synthetic-or-both/
ER -
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