Design of an Iterative Multi-Layered Analytical Framework for High-Precision Data Analytics and Business Decision Optimizations

Abstract

Real-time high-accuracy business decision-making requires these latest analytical frameworks to oral complex systems of enterprise data samples. Traditional data analysis methods are often based on homogeneous data samples, indifferent to contextual behaviors, and generally lack dynamic validation frameworks; hence, their accuracy, causal interpretability, and subsequent decision support remain questionable and severely flawed. Furthermore, all existing approaches inadequately account for the trade-off between data fidelity, computational efficiency, and actionable intelligence under dynamic business conditions. To address these shortcomings, we present a new multi-layered framework, which we call Data Analytics for Business Decision Making (DA-BDM), incorporating no less than five analytical innovations meant to serve toward ensuring maximum decision accuracy, validation robustness, and interpretability. The second module, Multi-Fidelity Reinforced Decision Analytics (MFRDA), uses reinforcement learning to balance low- and high-fidelity data analyses in decision-making for resource constraints. Ensemble Semantic-Behavioral Embedding Networks (ESBEN) learn deep semantic and behavioral patterns from structured documents and user activity logs through graph-attention-based fusion to advance context-aware analytics. The validation is performed using the Adaptive Evolutionary Validation Framework (AEVF) that jointly evolves the model parameters and the metric weights to maximize the multi-objective performance sets. The identification of causal structures within decision paths is the work of the Graph-Driven Causal Inference Engine (GCIE), which builds dynamic event-KPI graphs based on structural attention and Granger causality. Finally, Latent Multi View Decision Intelligence (LMDI) integrates financial, operational, and sentiment data via contrastive multi-view tensor factorization to predict optimal decisions. Experimental results show that DA-BDM demonstrated a 94% precision, 92% recall, and 27% enhancement on predicted results when compared with current leading methods. DA-BDM builds a strong technical foundation for the next generation's enterprise decision intelligence sets while enhancing interpretability, scalability, and real-time applicability of the proposed analytic framework sets.