ArticleOpen Access http://dx.doi.org/10.26855/acc.2025.12.007
AI-enabled Methods for Business Data Analytics Accuracy Enhancement and Emerging Advances
Kaixin Tian
FedEx, Pittsburgh, PA 15275, USA.
*Corresponding author: Kaixin Tian
Published: December 23,2025
Abstract
The rapid development of artificial intelligence (AI) has significantly transformed modern analytics, enabling higher accuracy, reliability, and efficiency in data-driven decision-making. This article explores the theoretical foundations and emerging methodologies of AI for enhancing data quality and analytical performance. Key contributions include AI-supported data quality optimization, encompassing automated cleaning, outlier detection, noise filtering, and integration of heterogeneous datasets. Furthermore, the application of generative AI and synthetic data addresses limitations of scarce or sensitive data, improving model generalization and predictive robustness. Advanced analytical frameworks, including explainable AI, multimodal integration, and edge–cloud collaborative processing, provide interpretability, real-time capabilities, and comprehensive insights across diverse data sources. By systematically combining these techniques, AI offers a holistic approach to mitigating traditional challenges in data quality, model transparency, and computational efficiency. The study emphasizes the potential of AI to establish a more reliable, adaptive, and context-aware analytical ecosystem, setting new standards for high-accuracy analytics in business and scientific environments.
Keywords
Artificial Intelligence; Data Quality Optimization; Generative AI; Synthetic Data; Explainable AI; Multimodal Analytics; Edge–Cloud Collaborative Analytics; High-Accuracy Analytics
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How to cite this paper
AI-enabled Methods for Business Data Analytics Accuracy Enhancement and Emerging Advances
How to cite this paper: Kaixin Tian. (2025) AI-enabled Methods for Business Data Analytics Accuracy Enhancement and Emerging Advances. Advances in Computer and Communication, 6(5), 298-303.
DOI: http://dx.doi.org/10.26855/acc.2025.12.007