Predictive Analytics and Big Data Integration for Strengthening ERP Systems Against Cybersecurity Threats - KishanKumar Routhu; Suneel Babu Boppana; Krishna Madhav Ja; Vasu Velaga; Varun Bodepudi; Laxmana Murthy Karaka (Utilitas Mathematica, 2024)
This paper contributes to and extends the existing literature on the importance of predictive analytics within Enterprise Resource Planning (ERP) systems by providing a holistic view of potential threats and optimal mechanisms associated with their implementation. However, this study is not a panacea, as it admits that achieving the aforementioned objectives presents a considerable challenge. We claim that academic researchers, developers, companies, regulators, and lawmakers must enter into a collaboration scheme that enables the development of the best designs of predictive models and integration strategies of Big Data into ERP systems. Nowadays, as the numerical output exceeds the analytical capacity of many ERP users, predictive analytics help to convert raw Big Data into valuable and easily understandable strategic information in real-time. The goal of this paper is to describe the benefits associated with predictive analytics and Big Data quality. Additionally, we introduce a conceptual model to demonstrate how Big Data can be integrated into ERP, increasing the robustness and resilience of both predictive analytics about the occurrences of potential threats and the correlates, alerts, and responses.