Analyzing Barriers to Data-Driven Governance in Iraq’s Public Policymaking: Evidence from COVID-19 Crisis Management in Salah al-Din Province

Ali Hamidizadeh; Ahmad Mohammad  Shahab

Authors

Ali Hamidizadeh Associate Professor, Department of Public Administration, Faculty of Management & Accounting, University of Tehran, Tehran, Iran Corresponding Author https://orcid.org/0000-0003-4489-570X
Ahmad Mohammad Shahab PhD Student, Department of Public Administration, Faculty of Management and Accounting, Farabi College, University of Tehran, Tehran, Iran Author https://orcid.org/0009-0007-5180-4630

Keywords:

Data-Driven Governance; Public Policymaking; COVID-19; Crisis Management; Big Data; Digital Governance; Salah al-Din Province; Iraq

Abstract

This study aimed to analyze the technological, institutional, human-resource, legal, ethical, socio-cultural, and political-administrative barriers to data-driven governance in Iraq’s public policymaking through the experience of COVID-19 crisis management in Salah al-Din Province. This qualitative, applied, descriptive-analytical study was conducted in Salah al-Din Province, Iraq. Participants were 18 individuals with relevant professional experience in public administration, healthcare management, information technology, data management, digital governance, policymaking, and COVID-19 crisis response. Participants were selected through purposive sampling, and data collection continued until thematic saturation was achieved. Data were collected through individual semi-structured interviews focused on digital infrastructure, data quality, interagency coordination, analytical capacity, legal and ethical considerations, organizational culture, and crisis-related evidence use. Interviews were recorded with consent, transcribed, and analyzed using thematic analysis through familiarization, initial coding, theme development, theme review, theme definition, and interpretation. Credibility, dependability, confirmability, and transferability were strengthened through systematic documentation, constant comparison, careful transcription, reflexive analysis, and cross-checking of emerging themes. The analysis identified five interrelated categories of barriers: technological and data-infrastructure limitations, institutional and organizational fragmentation, human-resource and analytical-capacity limitations, legal and ethical data-governance challenges, and socio-cultural and political-administrative barriers. Institutional fragmentation emerged as the most pervasive barrier, followed by weak data quality and standardization, lack of interoperability, bureaucratic delays, shortages of specialized analytical personnel, insufficient training, ambiguous data-sharing rules, privacy and cybersecurity concerns, resistance to digital transformation, weak interorganizational trust, and continued reliance on authority- and experience-based decision-making. During the COVID-19 crisis, these barriers contributed to delayed situational awareness, inconsistent public-health indicators, difficulty forecasting healthcare demand, inefficient resource allocation, and predominantly reactive rather than anticipatory crisis management. Data-driven governance in Salah al-Din Province is constrained by a systemic combination of technological, institutional, human, regulatory, and cultural weaknesses, indicating that effective reform requires integrated improvements in digital infrastructure, data interoperability, analytical capacity, interagency coordination, legal safeguards, and evidence-oriented administrative practices.

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