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Noise Reduction in data analysis refers to filtering out irrelevant, redundant, or misleading information so decision-makers can focus on what matters most. In financial contexts, Hermes AI uses noise reduction to eliminate non-impactful news from its alert feed. In customer service, Yumi AI applies it by categorizing and prioritizing tickets based on urgency and relevance. Noise reduction improves efficiency, reduces decision fatigue, and enhances accuracy in any AI-driven process. Effective noise reduction combines algorithmic filtering, sentiment analysis, and human oversight to ensure important signals aren’t lost.
