Data Governance Barriers to Reliable Decision Support in Brazil’s Public Sector

Abstract

Reliable decision support in public organizations depends less on the sophistication of dashboards than on the governance of the data that feed them. This study examines how data-governance barriers undermine Decision Support Systems (DSS) in Brazil’s public sector. Evidence from experienced information technology managers and coordinators in federal and state organizations identifies six interconnected barriers: limited data-governance awareness, resistance to change, capability gaps and staff turnover, fragmented responsibilities and policies, legacy-system and infrastructure constraints, and difficulties operationalizing security and privacy requirements. The findings show that these barriers do not act independently. Fragmented accountability weakens standards and ownership; turnover erodes institutional memory; legacy environments intensify manual reconciliation; and weak governance awareness reduces adherence to metadata, quality, and access routines. These mechanisms propagate into inconsistent definitions, duplicated records, limited traceability, delayed access, and reduced integration, directly lowering managerial trust in DSS outputs. The analysis advances a barrier-interaction model and a three-stage capability roadmap—stabilize, standardize and integrate, and institutionalize and scale—centered on stewardship, metadata, data quality, interoperability, leadership sponsorship, and auditable access. The study shows that DSS reliability is an organizational governance outcome, not merely a technical property of information systems. For public managers, the results indicate that trustworthy decision support can be strengthened by prioritizing critical data and institutionalizing governance routines before expanding technology investments.