AI-BASED NORMALIZATION METHODOLOGY FOR COLLECTING AND PROCESSING KPI INDICATORS
Keywords:
KPI, normalization, artificial intelligence, data cleaning, automation, NLP, scaling.Abstract
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.
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