Data quality remains the critical bottleneck in enterprise machine learning pipelines. Unreliable, schema-broken, drifted, or regulatory non-compliant data causes downstream analytics failures with consequences ranging from inaccurate predictions to regulatory penalties. This paper presents ADAP (Automated Data Analytics Platform) (Data Intelligence Pipeline with Expert Verification), an end-to-end data intelligence platform that unifies multi-source data ingestion, NLP-augmented semantic schema classification, seven-dimensional parallel validation, regulatory compliance enforcement (AML, HIPAA, SOX, GDPR), AutoML with SHAP explainability, and a dual reinforcement learning (RL) adaptation engine — all within a single auditable medallion-architected system. ADAP (Automated Data Analytics Platform) achieves schema classification accuracy of 94.7% across 31 semantic types, anomaly detection AUROC of 0.961, calibrated confidence scoring with ECE 0.0225 and AUC 0.9784, and multivariate drift detection at 89.4% accuracy at moderate distributional shift. A PPO Actor-Critic agent pre-trained over 1,000 synthetic episodes and warm-started via Thompson Sampling adapts 8-axis pipeline execution strategies in real time. End-to-end pipeline latency is under 7.4 seconds for 100,000-row datasets. All six production models pass tightened v7 quality gates, with performance validated end-to-end on held-out enterprise datasets.
Sanklecha et al. (Thu,) studied this question.