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Open Access
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Om Santosh Dhumal, Tanmay navanath bhor
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- Research Scholar, Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, Maharashtra, India, India
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Abstract
nThe abstract provides a concise overview of the paper. In this study, we explore the significance of Big Data Analytics (BDA) in enhancing decision-making processes within Business Intelligence (BI) frameworks. It involves processing vast volumes of data from various sources, enabling businesses to identify patterns, trends, and correlations that were previously unnoticed. This analytical power enhances strategic planning, customer understanding, operational efficiency, and competitive advantage. Through advanced algorithms and machine learning techniques, businesses can predict future trends, optimize operations, and personalize customer experiences. Furthermore, Big Data Analytics fosters a culture of data-driven decision-making, ensuring that strategies are aligned with actual market dynamics and consumer behavior, thereby significantly increasing organizational agility and innovation. We investigate the role of BDA in transforming raw data into actionable insights, contributing to more informed and effective decision-making in the business realm.
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Keywords: Bigdaa Analytics, Business Intelligence, Decision-Making, Data Transformation, Information Processing, Technology Integration.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Advanced Database Management & Systems(joadms)]
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References
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[1] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
[2] Davenport, T. H., & Harris, J. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
[3] Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70-76.
[4] Dumbill, E. (2012). Making sense of big data. Big Data, 1(1), 1-2.
[5] Inmon, W. H. (2005). Building the data warehouse. Wiley.
[6] Kimball, R., & Ross, M. (2002). The data warehouse toolkit: The complete guide to dimensional modeling. Wiley.
[7] LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-32.
[8] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
[9] Marz, N., & Warren, J. (2015). Big data: Principles and best practices of scalable Realtime data systems. Manning Publications.
[10] McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
[11] Zikopoulos, P., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2011).
[12] Understanding big data: Analytics for enterprise class Hadoop and streaming data. McGraw-Hill.
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Journal of Advanced Database Management & Systems
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 29, 2024 | |
| Accepted | March 21, 2024 | |
| Published | April 3, 2024 |
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