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All Journal The Eastasouth Journal of Information System and Computer Science
Dahule, Pratik
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Computer Science & IT

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The Strategic Impact of Project Management and Kanban in Enhancing Data Analysis Efficiency Dahule, Pratik
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.494

Abstract

In data analysis projects, effective project management is critical to ensuring timely execution, resource optimization, and quality deliverables. This paper explores the integration of project management principles with the Kanban methodology to enhance workflow efficiency, task prioritization, and cross-functional collaboration. By providing a structured yet flexible approach, Kanban enables teams to visualize processes, limit work in progress, and mitigate bottlenecks. A case study from a utility company illustrates the practical application of Kanban, highlighting its impact on improving operational efficiency, reducing resolution times, and increasing customer satisfaction. Through data-driven techniques such as cohort analysis and sentiment analysis, the study evaluates internal performance improvements and shifts in customer perception. The findings demonstrate that Kanban, when coupled with data-driven decision-making, can significantly enhance project execution and service quality in data-intensive environments. This paper contributes to the growing body of research on agile project management strategies for data analysis initiatives.
Analyzing Energy Consumption Data to Optimize Efficiency in High-Performance Computing Centers Dahule, Pratik
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.514

Abstract

High-performance computing (HPC) centers are at the forefront of technological innovation, enabling breakthroughs in fields ranging from scientific research to artificial intelligence. However, the immense computational power they deliver comes at a cost: these facilities consume vast amounts of energy, leading to soaring operational expenses and significant environmental footprints. As the demand for HPC capabilities continues to grow, optimizing energy efficiency has become a critical priority not only to cut costs but also to align with global sustainability goals. This article delves into how energy consumption data analysis can serve as a game-changer for HPC centers striving to balance performance with efficiency. By harnessing advanced tools such as real-time energy monitoring, machine learning algorithms, and predictive analytics, these facilities can unlock new opportunities for optimization. Data-driven strategies enable smarter workload distribution, more efficient cooling systems, and better utilization of hardware resources, all while maintaining the high-performance standards required for complex computations. To illustrate the real-world impact of these approaches, the article presents a case study of an HPC center that successfully implemented energy optimization strategies. Through a combination of cutting-edge analytics and strategic adjustments, the center achieved a notable reduction in power consumption without compromising computational performance. This example underscores the transformative potential of data-driven energy management in HPC environments, offering valuable insights for other facilities looking to enhance their sustainability and operational efficiency. By embracing these innovative techniques, HPC centers can not only reduce their energy costs but also contribute to a greener, more sustainable future, proving that high performance and environmental responsibility can go hand in hand.
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