AI-BASED NORMALIZATION METHODOLOGY FOR COLLECTING AND PROCESSING KPI INDICATORS - Shuhratov Mamurjon Shuhrat o‘g‘li (INTERNATIONAL JOURNAL OF SOCIAL SCIENCE & INTERDISCIPLINARY RESEARCH ISSN: 2277-3630 Impact factor: 8.036, 2025)
The heterogeneity of employee performance data collected in organizations—stemming from variations in format, structure, and recording methods—creates significant inaccuracies within KPI systems. This article proposes an AI-based normalization methodology aimed at standardizing KPI data, automatically filtering noisy and inconsistent entries, and converting heterogeneous inputs into a unified mathematical representation. The study employs NLP techniques, min–max scaling, z-score standardization, Isolation Forest, and sentence-embedding models. Experimental results demonstrate that the proposed normalization pipeline increases data accuracy from 78% to 94% and reduces the KPI calculation time from 40 hours to 0.8 hours.