Authors: Daniel Sørensen, Hiroshi Nakamura, Dr. Matteo Rinaldi, Elena Petrova, Ananya Kulkarni
Abstract: Large organizational information systems were historically designed to record and process structured transactions across business functions such as finance, supply chain, and human resources. While these systems ensured operational consistency and data reliability, their architecture primarily focused on transaction processing rather than continuous intelligence generation. Growing data volumes, distributed digital infrastructures, and the need for rapid decision making now require ERP environments to evolve beyond batch reporting and static analytics. This research presents an engineering framework for transforming transaction oriented ERP systems into data centric intelligence ecosystems through the integration of streaming analytics, DevOps automation, and predictive modeling. The proposed architecture enables continuous data ingestion, real time analytics pipelines, automated deployment of analytical services, and embedded predictive intelligence capable of supporting proactive operational decisions. By integrating data engineering principles with scalable analytics infrastructures, the framework demonstrates how operational data streams can be converted into actionable insights that improve forecasting accuracy, operational efficiency, and organizational responsiveness. The study contributes a unified approach for designing ERP ecosystems that support both reliable transaction processing and continuous intelligence generation within complex digital enterprises.
DOI: https://doi.org/10.5281/zenodo.19105020
