Chapter One — 1.1 Background to the Study

The rapid evolution of Predictive Analytics has transformed the way organizations design, deploy, and manage enterprise resource planning (ERP) systems. As institutions seek to modernize legacy processes, Predictive Analytics offers new opportunities to improve service delivery, reduce manual overhead, and respond more effectively to user needs.

Despite this potential, many existing enterprise resource planning (ERP) systems were not originally designed with predictive analytics in mind, resulting in persistent gaps in data security that limit their overall effectiveness. This study examines how Predictive Analytics can be applied to help close that gap.

1.2 Statement of the Problem

Current enterprise resource planning (ERP) systems in many organizations struggle with inadequate data security, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating predictive analytics, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a predictive analytics-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a predictive analytics-based approach to improving data security in enterprise resource planning (ERP) systems.
2. To evaluate the effectiveness of Predictive Analytics in enhancing data security within enterprise resource planning (ERP) systems.
3. To identify the key requirements and constraints relevant to deploying predictive analytics in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can predictive analytics be applied to improve data security in enterprise resource planning (ERP) systems?
2. How effective is Predictive Analytics at enhancing data security within enterprise resource planning (ERP) systems?
3. What requirements and constraints are relevant to deploying predictive analytics in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in predictive analytics for their own enterprise resource planning (ERP) systems, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.

1.6 Scope of the Study

The study is limited to the design, implementation, and evaluation of a predictive analytics-based approach to improving data security within enterprise resource planning (ERP) systems. Reflecting its BSc-level scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.