Using Machine Learning Techniques in Business Analytics: Lessons from Agile Project Management

A potent technique for drawing conclusions from data and advancing corporate analytics is machine learning (ML). Although successful ML project implementation calls for more than simply academic know-how; it also calls for agile approaches, efficient project management, and strategic planning. This study examines the relationship between agile project management and machine learning adoption, offering suggestions for how to include ML throughout business data analysis projects. In the larger context of business analytics, we point out important factors, obstacles, and tactics for streamlining the value of machine learning initiatives by referencing agile methodology principles and best practices. This paper seeks to provide enterprises with the expertise and resources needed to effectively employ machine learning (ML) for business analysis and gain a competitive edge by outlining efficient implementation methodologies.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/ic3i61595.2024.10828636
Citations
14
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
7.40
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article

Using Machine Learning Techniques in Business Analytics: Lessons from Agile Project Management

Baljinder Kaur, Gagandeep Singh Cheema, Tiyas Sarkar, Harjeet Kaur et al.
14 citations
Big Data and Business Intelligence
7.40
article

Using Machine Learning Techniques in Business Analytics: Lessons from Agile Project Management

Baljinder Kaur, Gagandeep Singh Cheema, Tiyas Sarkar, Harjeet Kaur, Amanpreet Singh, S. S. Das
article en
14 citations

Abstract

A potent technique for drawing conclusions from data and advancing corporate analytics is machine learning (ML). Although successful ML project implementation calls for more than simply academic know-how; it also calls for agile approaches, efficient project management, and strategic planning. This study examines the relationship between agile project management and machine learning adoption, offering suggestions for how to include ML throughout business data analysis projects. In the larger context of business analytics, we point out important factors, obstacles, and tactics for streamlining the value of machine learning initiatives by referencing agile methodology principles and best practices. This paper seeks to provide enterprises with the expertise and resources needed to effectively employ machine learning (ML) for business analysis and gain a competitive edge by outlining efficient implementation methodologies.

Lovely Professional University (IN)
Openalex Percentile: Top 3%
Big Data and Business Intelligence
7.40
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