American Journal of Advanced Multidisciplinary Research and Innovation

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The Role of Big Data Analytics in Transforming Business Intelligence and Organizational Decision-Making

Author(s) Marti Hearst
Country United States
Abstract The exponential growth of digital data has fundamentally transformed how organisations generate insights, formulate strategies, and achieve competitive advantage. Big Data Analytics (BDA) has emerged as a critical driver of Business Intelligence (BI), enabling organisations to process vast volumes of structured and unstructured data to support evidence-based decision-making. Advances in Artificial Intelligence (AI), Machine Learning (ML), cloud computing, Internet of Things (IoT), data warehousing, predictive analytics, and data visualisation have significantly enhanced the capability of organisations to analyse customer behaviour, optimise operations, forecast market trends, manage risks, and improve organisational performance. Business Intelligence systems integrated with Big Data Analytics provide real-time dashboards, interactive reporting, predictive modelling, and strategic insights that facilitate agile and informed managerial decisions across diverse industries.
This study investigates the role of Big Data Analytics in transforming Business Intelligence and organisational decision-making using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, industry reports, international publications, and multidisciplinary case studies. The study examines the architecture of Big Data Analytics, emerging analytical techniques, enterprise BI frameworks, and cross-sector applications in finance, healthcare, manufacturing, retail, supply chain management, education, public administration, and smart cities. It further analyses the integration of AI, cloud computing, data lakes, edge computing, blockchain, and explainable analytics in modern Business Intelligence ecosystems.
The findings indicate that Big Data Analytics significantly enhances organisational agility, operational efficiency, customer relationship management, strategic planning, financial performance, and innovation capability. Predictive analytics, descriptive analytics, diagnostic analytics, and prescriptive analytics enable organisations to identify hidden patterns, optimise business processes, detect fraud, improve forecasting accuracy, and support proactive decision-making. Cloud-based Business Intelligence platforms further improve scalability, accessibility, and collaborative analytics while reducing infrastructure costs.
Despite these opportunities, organisations face challenges including data privacy concerns, cybersecurity risks, poor data quality, integration complexity, talent shortages, ethical issues, regulatory compliance, and governance limitations. The study concludes that successful implementation of Big Data Analytics requires robust data governance, advanced analytical capabilities, responsible AI practices, secure digital infrastructure, and continuous organisational learning. These factors collectively enable intelligent Business Intelligence systems capable of supporting sustainable organisational growth and competitive advantage in the digital economy.
Keywords Big Data Analytics, Business Intelligence, Organizational Decision-Making, Artificial Intelligence, Machine Learning, Predictive Analytics, Data Science, Cloud Computing, Digital Transformation, Business Analytics.
Field Engineering
Published In Volume 3, Issue 2, March-April 2021
Published On 2021-03-06
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