NATIONAL-SCALE DATA GOVERNANCE DASHBOARDS: REAL-TIME QUALITY, LINEAGE, ACCESS, AND COMPLIANCE FOR SAUDI ARABIA (SDAIA-ALIGNED)

Abstract

National data programs increasingly depend on governance that is visible, measurable, and actionable at scale. In Saudi Arabia, this need is intensified by Vision 2030, the Saudi Data and Artificial Intelligence Authority (SDAIA), the National Data Management Office (NDMO), the Personal Data Protection Law (PDPL), the National Data Index (NDI) Operational Excellence model, and the Digital Government Authority (DGA) policy stack, all of which require stronger evidence that data is accurate, discoverable, traceable, lawfully accessed, and continuously compliant. This review paper revises and tightens the original manuscript into an approximately 5,000-word journal-style paper while preserving its core argument: national data governance dashboards should be treated not as reporting screens but as operating systems for data governance. Using a PRISMA 2020-guided narrative systematic review, the study synthesizes scholarly and official sources published from 2020 to early 2026. The paper contributes four advances. First, it makes Saudi policy alignment explicit by mapping PDPL, NDMO standards, NDI Operational Excellence, and DGA policies to measurable indicators. Second, it converts the prior conceptual framework into a practical federated operating model that includes a governance signal schema, sample OpenAPI structure, and reference data model for assets, ownership, lineage, and evidence. Third, it distinguishes critical from non-critical datasets and leading from lagging indicators, enabling more realistic dashboard design. Fourth, it proposes a 180-day pilot roadmap for 2-3 ministries and identifies implementation risks including metric gaming, uneven organizational maturity, metadata fragmentation, and privacy-aware observability. The review concludes that Saudi Arabia already has the institutional basis to move from policy-centric governance to evidence-based national data governance, especially as artificial intelligence becomes a strategic public-sector priority. The main implementation challenge is no longer writing policies but operationalizing machine-readable controls, shared metadata standards, and interoperable evidence flows across federated entities.