SDAF: Strategic Data Governance for Cloud-Enabled Big Data Analytics

Cloud-enabled big data programs in hybrid environments often underdeliver, and the conventional assumption that tooling or analytics maturity is the primary constraint is beginning to show cracks. Instead, the limiting factor is the frequent misalignment between governance arrangements and corporate strategy, compounded by ambiguous risk ownership and weak operational integration. This study develops a Strategic Data Alignment Framework (SDAF) for small and medium Caspian Basin seaports to translate this alignment problem into an executable implementation logic. Using an interpretive qualitative design, 14 semi-structured interviews were conducted with port deci-sion-makers, strategy and IT leaders, and relevant regulators, and the transcripts were analyzed to surface recurring mechanisms of failure. The data reveal consistent break-downs: unclear accountability for data decisions, immature stewardship, siloed collabo-ration, capability shortfalls, and under-instrumented feedback loops that fail to connect governance controls to the business outcomes. The SDAF addresses these mechanisms through a six-step pathway: (1) diagnose strategy–data misalignment, (2) establish minimum viable governance foundations, (3) specify decision-critical use cases and the role of big data in strategy, (4) embed governance into planning and performance cycles, (5) execute and monitor via measurable KPIs and auditable decision metrics, and (6) it-eratively review and improve through a continuous alignment loop, calibrated to reg-ulatory context, platform maturity, and risk appetite.