Big Data-Driven Decision Models for Corporate Sustainability: A Strategic Approach

In the era of data abundance and global sustainability challenges, corporations are increasingly leveraging big data analytics to inform strategic decision-making. This paper explores how big data-driven decision models contribute to corporate sustainability, focusing on environmental, social, and governance (ESG) objectives. By integrating structured and unstructured data from various sources—such as IoT sensors, customer interactions, supply chain operations, and environmental reports—firms can develop real-time, predictive, and prescriptive models that optimize sustainability outcomes. The study presents a strategic approach to embedding big data within sustainability frameworks, enabling organizations to monitor emissions, reduce operational waste, forecast risk, and align with stakeholder expectations. Through a combination of literature review, model-based conceptualization, and real-world case analysis, the paper examines how big data supports decision-making in resource management, regulatory compliance, ethical sourcing, and stakeholder transparency. It further analyzes how machine learning, cloud platforms, and data visualization tools empower firms to convert raw data into actionable sustainability insights. The research findings reveal that organizations adopting big data for sustainability not only experience environmental benefits but also gain strategic advantages such as cost efficiency, brand equity, and competitive positioning. The paper concludes with a proposed model that integrates big data into corporate sustainability strategy and recommends policy-level and technological interventions to scale its implementation. This study contributes to bridging the gap between digital transformation and sustainable strategic management, providing a roadmap for future-ready businesses.