Self-Service Analytics 2.0: AI-Powered Dashboard Generation with Human-in-the Loop Feedback Architecture
Keywords:
: Explainable AI, Cyberbullying, Real-Time NLP, Multi-Teacher Knowledge Distillation, XGBoost, SHAP, Emotion Detection, Sarcasm Detection, Multilingual NLP, conscious language use, symmetry principle, , positional labelling, computational linguistic encoding, syllable typology, formal notation system, rhythm-based phonology, meter and linguistic melody, ӭagyar MᲩa-siralom, Planctus ante nescia, speech processing, NLP., Compliance, Internet privacy, third-party vendors, data breaches, GDPR, CCPA, HIPAA, PCI DSS, vendor risk management, supply chain security, Business Intelligence Architecture, Human-in-the-Loop Systems, Automated Analytics, Dashboard Generation, Feedback MechanismsAbstract
This paper presents the architectural foundation and implementation results of Self-Service Analytics 2.0, an AI-powered system that automatically generates business dashboards from raw data while incorporating continuous human feedback loops. Our architecture integrates automated schema detection, intelligent KPI discovery, and adaptive visualization generation through a multi-layered feedback mechanism that learns from user interactions. The system demonstrates a 47% reduction in dashboard creation time and achieves 78% user satisfaction scores through iterative refinement. We detail the comprehensive architecture including feedback collection pipelines, model adaptation mechanisms, and human-in-the-loop quality assurance workflows that ensure generated insights remain aligned with business objectives.
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