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Big Data Analytics Frameworks for Sustainable Resource Management

Author(s) Prof. Dr. Arnold Tukker
Country Netherlands
Abstract The increasing pressure on natural resources, rapid urbanization, industrial expansion, population growth, and environmental uncertainty has created a critical need for intelligent and data-driven approaches to sustainable resource management. Big Data analytics provides an opportunity to integrate and analyze large volumes of structured, semi-structured, and unstructured information generated through Internet of Things devices, smart meters, satellites, environmental sensors, geographic information systems, enterprise platforms, and digital infrastructures. This study examines Big Data analytics frameworks for sustainable resource management and proposes an integrated conceptual framework connecting data acquisition, data integration, preprocessing, scalable analytics, predictive modeling, resource optimization, visualization, and decision support. A conceptual and descriptive research design was adopted through a systematic examination of scholarly literature addressing Big Data analytics, sustainability, resource management, artificial intelligence, machine learning, Internet of Things, cloud computing, and smart infrastructure.
The analysis demonstrates that Big Data analytics can strengthen sustainable resource management by enabling continuous monitoring, demand forecasting, anomaly detection, consumption analysis, and intelligent allocation. Predictive analytics can support proactive decision-making by identifying future resource requirements, while machine-learning techniques can reveal complex relationships between resource consumption, environmental conditions, operational factors, and human behavior. The proposed framework establishes a continuous analytical cycle in which heterogeneous data are collected, integrated, processed, analyzed, interpreted, and transformed into resource-management actions. Applications include energy management, water conservation, sustainable agriculture, waste management, transportation, industrial resource optimization, and smart-city development. Despite these advantages, successful implementation is constrained by data-quality problems, interoperability limitations, cybersecurity threats, privacy concerns, infrastructure costs, energy consumption, and shortages of specialized expertise. The study emphasizes that technological deployment must be supported by effective data governance, organizational coordination, sustainability indicators, and human oversight.
Keywords Big Data Analytics, Sustainable Resource Management, Data Science, Resource Optimization, Predictive Analytics, Sustainability, Artificial Intelligence, Internet of Things, Smart Infrastructure, Environmental Analytics
Field Engineering
Published In Volume 2, Issue 4, July-August 2020
Published On 2020-07-14
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