Data Governance Integrative Strategy For Optimizing Poverty Reduction Programs In Garut District Using System Dynamic Modeling Approach

The issue of overlapping poverty data among government agencies in Garut Regency has led to ineffective policy implementation. This study aims to analyze the factors influencing data governance and design an integrative strategy to optimize poverty programs through a System Dynamics Modeling (SDM) approach. Using a qualitative method, this research develops a stock-flow diagram to capture structural mechanisms in data governance. Data collection was conducted through interviews with key informants, historical data analysis, and a fuzzy scale, then analyzed using Vensim software. The model refers to Abraham's Data Governance Theory, which includes Data Scope (Traditional Data and Big Data), Domain Scope, Antecedents (Internal and External), Structural Mechanisms, Relational Mechanisms, and Procedural Mechanisms. The simulation results show that improving data quality, metadata management, effective communication, and strengthening roles and responsibilities across agencies significantly reduce data discrepancies. Additionally, better decision-making coordination and performance monitoring mechanisms enhance data accuracy. The findings emphasize the importance of integrating data governance strategies through cross-sector collaboration, consistent policy implementation, and a sustainable monitoring system. This integrative approach effectively addresses data overlaps and improves the accuracy and efficiency of poverty alleviation programs, serving as a model for other regions facing similar challenges.