Data Flow Mechanisms and Model Applications in Intelligent Business Operation Platforms

Abstract

Intelligent business platforms integrate multi-source data flows with advanced analytical models to enhance organizational decision-making, operational efficiency, and strategic responsiveness. This study examines the core mechanisms of data circulation—including acquisition, transmission, storage, governance, and optimization—and analyzes how these processes support descriptive, predictive, and prescriptive analytics. By incorporating machine learning, deep learning, and reinforcement learning, intelligent platforms are able to extract actionable insights, automate decision processes, and adapt to dynamic business environments. The research further emphasizes the importance of synergy between data flows and model applications, demonstrating that model performance is closely tied to data quality, timeliness, and processing efficiency. Challenges such as data silos, model interpretability, scalability, and human–machine collaboration are also discussed, along with opportunities for future advancements. The findings highlight the need for adaptive data pipelines, intelligent decision loops, and AI-driven optimization strategies to build more resilient and autonomous business systems. This study provides a framework for understanding how data and models jointly shape the next generation of intelligent business operations.