<![CDATA[Indonesia’s digital government agenda establishes a policy backbone for data-driven and AI-enabled public services through the national Electronic-Based Government System (SPBE) and the One Data policy, while the Personal Data Protection Law (PDP) frames privacy-by-design obligations for public institutions. Building on international guidance (OECD’s G7 Toolkit; World Bank’s GovTech Maturity Index), this review synthesizes how Data Science (DS)—via reliable feature engineering and descriptive–predictive analytics—can be aligned with Artificial Intelligence (AI) for automation and decision support under public-sector accountability requirements. We identify recurrent enablers (interoperable data architecture, data governance, civil service capabilities, and MLOps) and barriers (data silos, legacy constraints, skills gaps, and explainability/ethics demands), and propose evaluation indicators that link model performance to service performance: service latency reduction, service quality, model fairness, and explainability. The contribution is a systems view that connects SPBE/Satu Data/PDP compliance to DS–AI operations across the lifecycle (governance ? pipeline/feature store ? training/validation ? deployment ? MLOps & audit), and a graduate-level research agenda on causal impact and federated collaboration across agencies.]]>
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