As AI systems increasingly rely on unstructured “conversation data” — from social media, reviews, and customer feedback — ensuring that data is accurate, unbiased, and trustworthy has never been more critical. This whitepaper, developed in collaboration with AMEC and the Global Data Quality Initiative, outlines key principles and frameworks for improving data quality across social and voice-of-customer intelligence.

It explores how poor data fuels misinformation, bias, and AI hallucinations, and presents actionable strategies to combat these challenges through Trusted AI, model observability, and data governance. The paper provides organizations with a roadmap to align with emerging global standards like the EU AI Act, OECD AI Principles, and ISO 20252, ensuring data used for AI and market research is both reliable and responsible.

A must-read for brands, analysts, and data leaders seeking to future-proof their AI initiatives and build trust in their insights ecosystem.