Digital government is moving from simple service digitization toward data-driven governance, where administrative records, sensor streams, service logs, mobile interactions, and open data are combined to improve decisions and public services. Recent studies show that governments see clear value in big data, yet practical adoption remains uneven because of fragmented data environments, uncertainty among managers, privacy concerns, weak standardization, legacy systems, and limited collaboration across agencies. This paper proposes a conceptual big data analytics framework for digital government that integrates five elements: data acquisition and stewardship, interoperable data infrastructure, analytics and intelligence, decision and service delivery, and cross-cutting governance and capability management. The framework is designed to help public institutions convert raw data into accountable action while preserving public values such as fairness, transparency, legality, and trust. The paper argues that digital government should not treat analytics as a stand-alone technology project. Instead, analytics capability must be embedded within institutional processes, stewardship models, and citizen-facing service design. A phased implementation roadmap is also presented, covering readiness assessment, pilot deployment, scaling, and continuous evaluation. The proposed framework contributes a practical and research-grounded structure for agencies seeking to operationalize big data in digital government settings.

Keywords: digital government; big data analytics; public sector innovation; data governance; open government data; public service delivery