In an era dominated by data-driven decision-making, the efficient analysis of vast datasets has become a critical necessity across industries. Manual data analysis, though thorough, is often time-consuming, error-prone, and struggles to keep up with the growing volume and complexity of data. Traditional manual analysis methods are not only labor-intensive but also susceptible to human errors, leading to potentially flawed conclusions. Automation of data analysis offers a promising solution to these challenges. By integrating advanced computational techniques such as data mining, into the analysis process, organizations can expedite their decision-making processes and unearth hidden patterns and trends within their data. This research project responds to the inefficiencies inherent in traditional manual data analysis methods, offering an automated solution to enhance the accuracy, speed, and scalability of decision-making processes across diverse industries. The project employs a framework capable of streamlining data analysis, overcoming the limitations of human-intensive processes. The proposed paradigm shift not only reshapes industries but also empowers analysts and decision-makers to focus on higher-order tasks requiring human intuition and expertise. The automated data analysis system demonstrated a remarkable increase in efficiency, resulting in a 30% reduction in processing time compared to traditional manual methods. Through the implementation of advanced algorithms, the system achieved a 20% decrease in error rates, showcasing its effectiveness in ensuring more accurate results. The automated system resulted in a 40% increase in analyst productivity, allowing professionals to focus on higher-order tasks and strategic decision-making.
Snehal Jadhav, Shriya Pathak, Suvarna Patil, Shivganga Gavhane
Dr. D.Y. Patil Institute of Engineering, Management and Research, India
Data Transformation, Automation, Data Analysis, Data Visualization, Data-Driven Decision-Making
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 5 , Issue: 2 , Pages: 573 - 578 )
Date of Publication :
March 2024
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103
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