International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Big Data and Business Intelligence: Key Drivers of Sustainable Development in USA-based Organizations

Author(s) Muhammad Riyaz Hossain, Fekede Gindaba, Abu Yusuf, Md Abu Musa Mrydha, Md Ziaur Rahman
Country United States
Abstract The current report assesses the effect of Big Data and Business Intelligence on sustainability initiatives and effectiveness of operations of an organization in the United States; specifically, it considers the cases of UPS and Walmart. This study employed the mixed-method approach, including a quantitative analysis by descriptive statistics and Chi-square tests besides carrying out a qualitative content analysis of case studies and literature. It would, therefore, emerge that UPS has managed to achieve this with a 7.1% decrease in its operational cost while lowering the release of CO2 emissions to a 5%. As regards Walmart, it also has an achievement on record about energy consumption by a decline of 20%, which further enhanced inventory turn around. The thematic analysis emerges in three themes as coming out: data-driven information into decision-making, attempts toward sustainability and environmental-related efforts, and how effectiveness comes about with process streamlining of operations. Despite these successes, challenges such as high initial investment costs and data privacy concerns prevent them from reaching wider use, especially among smaller organizations. Conclusion In this regard, it is realized that although Big Data and BI are of utmost importance in promoting sustainability, the financial and ethical challenges associated with them need to be solved before full realization can be achieved. The recommendations for the future include development of a Big Data strategy that could align with sustainability goals, manages investments and data governance-related problems in small companies.
Keywords Big Data, Artificial Intelligence, Business Intelligence (BI), Sustainability, Operational Efficiency, and Decision-Making
Field Business Administration
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-29
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.34253
Short DOI https://doi.org/g8xgk5
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