Smart ERP: Scalable Data Engineering Frameworks Using Artificial Intelligence

Abstract

Enterprise Resource Planning (ERP) systems are foundational to the functioning of large organizations, facilitating the seamless integration of diverse business functions such as finance, procurement, human resources, inventory, and sales. However, the exponential increase in volume, velocity, and variety of data generated from these modules has rendered traditional data engineering practices inadequate. This paper presents an AI-driven data engineering framework designed to address the challenges of data quality, scalability, and pipeline agility in modern ERP environments. The proposed framework incorporates machine learning models for intelligent metadata-aware ETL rule generation, unsupervised anomaly detection, and dynamic orchestration of end-to-end data pipelines. It also embeds core software engineering principles including modular architecture, CI/CD automation, observability, and testing to ensure reliability and maintainability. A proof-of-concept system was implemented using technologies such as Apache Airflow, Spark, AWS SageMaker, and Amundsen, and was evaluated on synthetic ERP data across procurement, payroll, and inventory modules. Experimental results reveal that the anomaly detection accuracy improved from 71% to 94% using the proposed framework, while ETL rule generation time decreased from two hours to just 15 minutes. Pipeline execution latency was reduced by 45%, and downstream model accuracy improved from 82% to 91% after automated data cleaning. These results confirm the framework’s effectiveness in improving data quality and operational efficiency. The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next generation enterprise software resilience and performance.