A Multi-Layer Business Analytics Model for Autonomous Decision Systems

Modern enterprises face a persistent structural gap between micro-level data generation and macro-level strategic execution: legacy business intelligence relies on static batch reporting that creates critical decision latency, while unconstrained autonomous decision systems (ADS) frequently execute automated actions without systematic risk governance or cloud security baselines. To resolve this trade-off, the Multi-Layer Business Analytics Model (MLBAM) establishes an integrated theoretical and computational framework across five connected layers: Data Aggregation & Telemetry, Feature Abstraction & Granularity Control, Algorithmic Optimization & Predictive Inference, Strategic Agility & Automated Execution, and ERM Governance & Cloud Security. By dynamically transforming granular telemetry into abstracted state vectors and subjecting machine learning policies to real-time risk boundaries, MLBAM balances operational execution speed with strict safety controls. Empirical validation using Partial Least Squares Structural Equation Modeling (PLS-SEM) across a benchmark simulation dataset ($N = 10,000$ operational cycles) reveals that data granularity exerts a strong positive effect on predictive analytical capabilities ($\beta = 0.482, p < 0.001$), which directly enhances enterprise strategic agility ($\beta = 0.514, p < 0.001$). Crucially, embedded enterprise risk management (ERM) functions as a vital moderating mechanism, reducing autonomous decision error rates by 68.4% under severe market volatility without causing prohibitive decision latency.