Research on the Fusion of Data Analysis Methods and Artificial Intelligence in Electronic Information Systems

Abstract

This research initially reveals that as digital transformation deepens across industries, electronic information systems have become core business hubs. The exponential growth of the global datasphere (IDC data: 120ZB in 2024, projected to exceed 150ZB in 2025) inherently places higher demands on the intelligence of the "data-decision-making" chain. The shortcomings of traditional statistical modeling and manual feature extraction are not only technical limitations but also hinder the industry's transition from "experience-driven" to "data intelligence-driven" decision-making. The integration of AI and electronic information systems is the key to overcoming this dilemma and reshaping decision-making efficiency. This article focuses on the theme of integration: first, analyzing the logical foundation and internal mechanisms to clarify the dimensions of AI empowerment; then, building a technical architecture and exploring data processing and algorithm adaptation paths; combining industrial fault diagnosis and financial risk assessment to verify effectiveness, simultaneously proposing data compliance and model risk management solutions, and ultimately refining optimization strategies. This study does not cover integration solutions for small and medium-sized enterprises with low computing power, and the applicability of the conclusions to hardware resource constraints remains to be verified. In the future, we can focus on the development of lightweight AI models (such as the optimized version of TensorFlow Lite) or launch low-cost integration toolkits to lower the threshold for intelligent transformation for small and medium-sized enterprises. This article finds that the integration of the two requires data quality as the cornerstone, algorithm innovation as the core, and compliance control as the barrier. It can not only improve the accuracy and real-time performance of system data analysis, but also promote its transformation from "data storage end" to "intelligent decision-making center", providing solid technical support for industrial innovation.