## 1 Introduction
This paper adopts the \"process sequencing\" approach [6; 7], which posits that events are causally connected within a public policy sequence [8; 9; 10; 11]. It can be argued that governments choose actions aimed at addressing issues identified as central, enabling analysts to perceive the phenomenon that requires explanation. The rational choice of an agent seeks to maximize values by selecting the intervention alternative that best facilitates the achievement of their goals and objectives [12]. However, it is essential to move away from the notion of absolute rationality in decision-making processes as public policy processes reveal, to some extent, how governments pursue certain objectives but not why those objectives are prioritized [13]. In summary, decisions are interconnected operations that involve selecting an alternative from a set of options, or more simply, the choice of a course of action [14]. In the context of policy analysis, institutional memory [15] refers to the accumulated knowledge, practices, and decisions that shape an organization's long-term strategy. It includes formal elements such as archives, records, and databases, as well as informal aspects like shared experiences, cultural norms, and expert intuition within policy institutions. The analogy between decision-making processes [16] can lead to repeating the formula that has been successfully applied in the past. Our framework operationalizes institutional memory through historical analysis and predictive modeling, ensuring that past policy successes and failures inform present decision-making. Organizational adaptability [17; 18] refers to an institution's ability to modify strategies and decision-making structures in response to external changes, policy shocks, and technological advancements. In our framework, adaptability is captured through predictive modeling, AI-assisted learning mechanisms, and intervention design, allowing organizations to respond proactively to emerging policy challenges while maintaining institutional stability. To provide a clear roadmap, this paper is structured as follows. In Section 2, we review the foundational literature in institutional theory and its applications to social systems, establishing the theoretical groundwork for our framework. Section 3 introduces our comprehensive framework for data-driven policy analysis, detailing its five core components and communication structure. In Section 4, we bridge theoretical foundations with practical applications, demonstrating how our methodology translates into concrete tools and processes. Section 5 showcases practical implementations in the hospitality sector, featuring advanced visualization techniques, graph neural networks, and LLMs. Section 6 further explores additional use cases from related policy domains: global food security and educational development. We then present our discussion in Section 7, analyzing the framework's effectiveness and implications across multiple dimensions. Finally, in Section 8, we conclude by summarizing our contributions and highlighting the framework's potential for broader application across different policy domains. Each section builds upon the previous ones to demonstrate how data-driven approaches can be effectively integrated with institutional theory to create robust, actionable policy insights while maintaining social order cohesion.