Data-Driven Decision-Making: Big Data Analytics & Machine Learning in M&A and IT Supply Chain

EasyChair Preprint 12080

Abstract

In today's rapidly evolving business landscape, the effective utilization of data has become paramount for informed decision-making. This paper explores the integration of big data analytics and machine learning techniques in the context of mergers and acquisitions (M&A) and the IT supply chain. By harnessing the potential of these advanced technologies, organizations can enhance their decision-making processes, improve operational efficiency, and gain a competitive edge in the market. The paper examines how data-driven approaches empower stakeholders to make more informed decisions throughout the M&A lifecycle, from due diligence to post-merger integration. Additionally, it explores the application of big data analytics and machine learning in optimizing IT supply chain operations, including inventory management, logistics, and demand forecasting. Through real-time data analysis and predictive modeling, businesses can better anticipate market trends, mitigate risks, and capitalize on new opportunities. The synergy between data-driven decision-making, big data analytics, and machine learning presents a transformative opportunity for organizations operating in the M&A and IT supply chain domains.

Keyphrases: Big Data Analytics, Business Transformation, Decision Optimization, IT supply chain, Mergers and Acquisitions (M&A), Strategic Initiatives, data-driven decision making, machine learning

@booklet{EasyChair:12080,
  author    = {Jonny Bairstow},
  title     = {Data-Driven Decision-Making: Big Data Analytics & Machine Learning in M&A and IT Supply Chain},
  howpublished = {EasyChair Preprint 12080},
  year      = {EasyChair, 2024}}