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In today's era of data-driven decision-making and sustainable development goals, the amalgamation of Machine Learning (ML) techniques with Master Data Management (MDM) stands as a promising avenue. This abstract explores the potential of ML-infused MDM methodologies in fostering sustainable development. By employing ML algorithms in data management, organizations can derive actionable insights, optimize resource utilization, and propel socio-economic and environmental sustainability. This paper delineates the synergies between ML and MDM, emphasizing their collective role in addressing challenges related to sustainability. Furthermore, it examines case studies and real-world applications where ML-powered MDM systems have contributed significantly to sustainable development initiatives. The study concludes by underlining the transformative potential of ML-driven MDM paradigms in advancing sustainable practices across various domains, paving the way for a more resilient and equitable future.

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Enhancing Sustainable Development Through Machine Learning-Driven Master Data Management

  • 会议论文

  • 发表日期：

    2025年

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  • Ronak Ravjibhai Pansara,
  • Balaram Yadav Kasula,
  • Ashima Bhatnagar Bhatia,
  • Pawan Whig

摘要

In today's era of data-driven decision-making and sustainable development goals, the amalgamation of Machine Learning (ML) techniques with Master Data Management (MDM) stands as a promising avenue. This abstract explores the potential of ML-infused MDM methodologies in fostering sustainable development. By employing ML algorithms in data management, organizations can derive actionable insights, optimize resource utilization, and propel socio-economic and environmental sustainability. This paper delineates the synergies between ML and MDM, emphasizing their collective role in addressing challenges related to sustainability. Furthermore, it examines case studies and real-world applications where ML-powered MDM systems have contributed significantly to sustainable development initiatives. The study concludes by underlining the transformative potential of ML-driven MDM paradigms in advancing sustainable practices across various domains, paving the way for a more resilient and equitable future.