Glossary
AI Hallucinations in Analytics: Why They Happen and How to Stop Them
In analytics, a hallucination is rarely a fabricated number. It is a correct number computed from the wrong definition — revenue joined the wrong way, retention measured against the wrong cohort, margin computed without the right exclusions. The answer feels plausible and is almost never caught.
Types of hallucination that matter for analytics
Definition hallucination — using a plausible but wrong definition of a metric.
Join hallucination — inferring a relationship between tables that is technically valid but semantically wrong.
Exception hallucination — ignoring a business rule that only the analysts knew about.
Confidence hallucination — delivering a visibly polished answer with no way to validate it.
Why prompting alone cannot fix it
Better prompts reduce obvious errors but cannot inject knowledge the model does not have. Enterprise business logic is not in the training data and will never be. The only sustainable fix is to put that logic in a governed, reviewable context layer the model is forced to consult.
A workable anti-hallucination recipe
Ground every answer in a context layer analysts own.
Make every answer traceable to the specific definitions and rules that produced it.
Keep humans in the loop for high-value questions, so validated patterns get promoted and reused.
Treat the trust layer as the product surface, not just plumbing.
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