<![CDATA[The accelerating digitalization of the energy sector has transformed data into a critical operational and strategic asset, enabling organizations to optimize performance, improve reliability, and support long-term sustainability goals. Despite the growing importance of data-driven decision-making, current industry practices frequently treat data quality as a universal objective, seeking to maximize all quality dimensions simultaneously regardless of context. This systematic synthesis of 50 empirical and conceptual studies published between 2012 and 2026 challenges this assumption by demonstrating that data quality priorities vary significantly according to decision-making requirements. The study employs a comprehensive literature review approach to examine how data quality dimensions influence operational and strategic decisions across diverse energy-sector applications. The analysis covers operational decision contexts, including real-time control systems, Supervisory Control and Data Acquisition (SCADA) platforms, fault detection mechanisms, predictive maintenance, and load-balancing operations, as well as strategic contexts such as capital investment planning, energy transition initiatives, regulatory compliance, risk management, and sustainability reporting. Findings reveal a clear divergence in data quality priorities. Operational decisions depend primarily on timeliness and accuracy to support rapid response and system stability, whereas strategic decisions place greater emphasis on completeness, consistency, and contextual integrity to ensure reliable long-term planning and governance. Based on these findings, this study proposes the Decision-Context Data Quality (DCDQ) Framework, which reorients data quality management from universal optimization toward context-sensitive prioritization. The framework provides technology managers, policymakers, and engineering practitioners with a structured methodology for aligning data quality investments with specific decision requirements, thereby reducing operational inefficiencies and financial costs associated with dimensional misalignment. Furthermore, the study highlights implications for data governance policies, engineering practices, and future research, emphasizing the importance of layered governance architectures, provenance-enabled data systems, and adaptive quality management strategies to support increasingly complex and data-intensive energy infrastructures.]]>
