Big Data Analytics and Predictive Intelligence for Evidence-Based Decision Making Across Multidisciplinary Domains

Dawn Song

Big Data Analytics and Predictive Intelligence for Evidence-Based Decision Making Across Multidisciplinary Domains

Author(s)	Dawn Song
Country	United States
Abstract	The rapid expansion of digital technologies, interconnected systems, cloud computing, Internet of Things (IoT) devices, social media, electronic records, and automated information systems has generated unprecedented volumes of structured and unstructured data. Big Data Analytics and predictive intelligence have consequently emerged as critical capabilities for transforming complex datasets into actionable knowledge and supporting evidence-based decision making. Conventional decision-making approaches that rely primarily on historical experience and limited datasets are increasingly inadequate for addressing complex, dynamic, and uncertain problems across contemporary multidisciplinary environments. Big Data Analytics enables organisations to collect, integrate, process, and interpret large-scale datasets, while predictive intelligence applies statistical modelling, machine learning, artificial intelligence, and advanced computational techniques to identify patterns and anticipate future outcomes. This study examines the role of Big Data Analytics and predictive intelligence in evidence-based decision making across multidisciplinary domains. A qualitative and analytical research methodology based on secondary literature is adopted to examine applications across healthcare, finance, education, agriculture, manufacturing, environmental management, public administration, marketing, transportation, and scientific research. Particular attention is given to the relationship between data quality, predictive modelling, artificial intelligence, decision support systems, explainability, data governance, and organisational performance. The study further investigates the challenges associated with data privacy, cybersecurity, algorithmic bias, interoperability, data quality, technological infrastructure, and digital skills. The findings indicate that Big Data Analytics and predictive intelligence can substantially improve forecasting accuracy, resource allocation, risk management, operational efficiency, strategic planning, and policy development. However, the effectiveness of data-driven decision making depends not only on sophisticated algorithms but also on data quality, human expertise, transparent governance, contextual interpretation, and responsible technology adoption. The study concludes that organisations should adopt integrated human–AI decision frameworks in which predictive intelligence augments rather than replaces human judgement. Such an approach can facilitate more adaptive, transparent, and evidence-based decision making across diverse multidisciplinary environments.
Keywords	Big Data Analytics, Predictive Intelligence, Evidence-Based Decision Making, Artificial Intelligence, Machine Learning, Data Science, Decision Support Systems, Multidisciplinary Analytics.
Field	Engineering
Published In	Volume 4, Issue 1, January-February 2022
Published On	2022-01-24

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Big Data Analytics and Predictive Intelligence for Evidence-Based Decision Making Across Multidisciplinary Domains

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