<![CDATA[The global digital transformation, accelerated by the COVID-19 pandemic and the massive adoption of generative artificial intelligence (AI) architectures, has triggered an exponential surge in the construction of hyperscale data center infrastructures. This growth brings a direct consequence in the form of escalating global energy consumption, positioning data centers among the most energy-intensive industrial facilities in the world. This research presents a comprehensive analysis of the role of Data Center Infrastructure Management (DCIM) as a holistic instrument to diagnose, orchestrate, and improve energy efficiency, quantified specifically through the reduction of the Power Usage Effectiveness (PUE) metric. Utilizing a methodological framework of systematic review and empirical multi-case analysis, this research deconstructs operational thermodynamic dynamics that cause persistent inefficiencies, such as server sprawl resulting in zombie servers and over-provisioning of HVAC systems. Quantitative analysis proves that DCIM intelligence features—including real-time thermal mapping, digital twin prediction, and capacity load optimization—can drastically reduce PUE, with several institutional facilities successfully lowering baseline PUE from 1.83 to 1.51, while cutting cooling loads by up to 48%. In Indonesia, DCIM integration proves highly crucial to fulfilling mandates of SNI 8799:2020 and SPBE guidelines, and mitigating peak load resilience risks for PT PLN (Persero). This research proposes a computing infrastructure governance paradigm focused on total automation of the thermal power supply chain to achieve globally competitive green infrastructure.]]>
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