When Data Reform Meets Bureaucratic Hierarchy: A Political Economy Analysis of Institutionalizing a District Health Data Bank in West Sumbawa District, Indonesia

Background Although often perceived as a technical tool, a data bank is fundamental for district performance management, facilitating integrated coordination, data consolidation, and routine information use for decision-making. However, implementation unfolds within hierarchical bureaucratic systems shaping authority distribution, workload allocation, coordination, and resource use. This study examines the political economy of institutionalizing a district-level health data bank in West Sumbawa District, Indonesia. Method A longitudinal qualitative case study was conducted in West Sumbawa District, West Nusa Tenggara Province, Indonesia, from November 2025 to March 2026 across three evaluation phases (baseline, midline, endline), involving 25 District Health Office (DHO) officers appointed to the Health Data Bank team, representing five organizational units, including the Secretariat, General and Human Resources Unit, and Public Health Division. Data were collected through participatory workshops, focus group discussions, in-depth interviews, observation, and document review, including official decrees, SOPs, meeting minutes, and implementation records, and analysed using Bossert's Decision Space Framework combined with a problem-driven political economy analysis examining how power relations, institutional norms, workload, resource support, and perceived incentives influenced whether technical reforms became operationalized in routine practice. Results The Health Data Bank progressed from strong institutional acceptance to structural formalization. Decision-making authority remained centralized within the Secretariat, enabling coordination but limiting distributed ownership. Resource constraints increased workload, concentrated in the Secretariat and division coordinators responsible for data consolidation and validation, without dedicated financing or staffing, despite improved analytical capacity. Accountability mechanisms were established through governance instruments, though enforcement and feedback loops remained underdeveloped. Data submission, validation, and use were not yet fully institutionalized, resulting in a gap between structural readiness and functional use. Conclusion Data institutionalization involves both technical and organizational processes, requiring collaborative negotiation of authority, workload, and resources. Continued attention to these factors will help structural formalization translate into sustainable operational outcomes.