Big Data-Driven Portfolio Simplification: Leveraging Self-Labeled Clustering to Enhance Decision-Making

Document Type

Conference Proceeding

Publication Date

12-4-2023

Abstract

In the evolving landscape of business analytical practice, big data stands as a pivotal force, steering organizational strategies, particularly in portfolio management across end-to-end businesses. With the surge in data's volume, variety, veracity and velocity, there is a pressing need for sophisticated computational methods to demystify intricate business portfolios, thereby facilitating astute decision-making. Traditional portfolio analysis techniques, although foundational, grapple with the challenges posed by expansive, multifaceted data and volatile market dynamics. To counter these challenges, our research pioneers an innovative approach, harnessing the power of clustering algorithms to refine and consolidate business portfolios. We employ big data techniques to analyze and categorize extensive portfolio datasets, unearthing inherent groupings and patterns. Leveraging clustering algorithms, we categorize business entities by similarity, yielding a streamlined and lucid portfolio blueprint. Our approach not only enhances the clarity of vast business portfolios but also strengthens strategic decision-making capabilities, propelling organizational nimbleness and market competitiveness. Through comparative analyses, our solution showcases significant advantages in portfolio simplification and decision-making efficacy over conventional techniques.

Identifier

85192194251 (Scopus)

ISBN

[9798400704734]

Publication Title

10th IEEE ACM International Conference on Big Data Computing Applications and Technologies Bdcat 2023

External Full Text Location

https://doi.org/10.1145/3632366.3632394

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