(Developing a Synergistic Data Science and AI Model to Enhance Decision-Making Accuracy in Data-Driven Organizations)

In the digital transformation era, organizational decision-making increasingly depends on data quality and AI analytical power. This study proposes an integrated framework synergizing data science with advanced AI algorithms to enhance managerial decision accuracy, speed, and robustness. Three-Layer Hybrid Model: 1. Enterprise Data Preprocessing & Enrichment: Structured (ERP, CRM) and unstructured (email, reports, sensors) data unified via advanced ETL, normalization, Isolation Forest anomaly detection, and BERT-based semantic augmentation. 2. Multimodal Deep Learning: Hybrid network fusing CNNs (temporal/visual features), Bi-LSTM + Multi-Head Attention (historical decision sequences), and lightweight Transformers (DistilBERT-style, textual processing), combined through an adaptive Dynamic Fusion Layer with learned gating. 3. Predictive Decision Optimization: Deep model output injected into PPO-based Deep Reinforcement Learning (DRL) within an organizational Markov Decision Process (MDP), learning optimal policies under resource, risk, and uncertainty constraints. Simulations on 10,000 synthetic dynamic system scenarios show 35% accuracy, 42% convergence speed, and 28% noise resilience gains over baselines (Random Forest + Rule-based DSS). The framework enables deployable, interpretable, and uncertainty-resilient intelligent decision-support systems in data-driven organizations.

Keywords:

formatting , data science , multimodal deep learning , DRL-PPO