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To come to important decisions, life sciences leaders increasingly rely on insight from a widening array of sources, which are being created at unprecedented speed. Between expert interviews, surveys, advisory panels, AI-generated synthesis, and third-party data, the volume and variety of data can be a boon to discovery, giving teams richer research outcomes and greater scale, which in turn expands the imagination for what’s possible in a given R&D program.
But the trend also makes it harder to ensure and audit the quality of data. Unqualified participants, unverified credentials, undisclosed conflicts, and fraudulent respondents can sneak into the workflow and compromise research, which comes with high stakes in healthcare. Flawed inputs can skew pricing, forecasting, positioning, and launch decisions worth millions, not to mention the human implications of drugs or technology designed on flawed research. In a society that’s increasingly concerned about the influence of AI on our life decisions and future, this represents an area where trust and safety are paramount.
So, how do teams establish confidence in the respondents and sources behind their research?
It’s a problem ...