Predictive Analytics and Big Data Integration for Strengthening ERP Systems Against Cybersecurity Threats
Keywords:
Predictive Analytics, Enterprise Resource Planning, ERP Systems, Big Data, Integration Strategies, Holistic View, Potential Threats, Optimal Mechanisms, Collaboration Scheme, Predictive Models, Real-Time Information, Strategic Information, Big Data Quality, Conceptual Model, Robustness, Resilience, Potential Threats Occurrences, Alerts, Responses, Analytical Capacity.Abstract
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.
