HARNESSING THE POWER OF AI AND MACHINE LEARNING IN CLOUD DATA WAREHOUSING: TRANSFORMING DATA INTO INSIGHTS

This article explores the transformative integration of artificial intelligence (AI) and machine learning (ML) with cloud data warehousing, examining its profound impact on data management and analytics across industries. It outlines the key benefits of this integration, including enhanced data management, improved analytical capabilities, and generation of actionable insights. The article discusses industry-specific use cases in finance, healthcare, and retail, highlighting tangible improvements in areas such as risk assessment, patient care, and inventory optimization. Additionally, it addresses critical challenges in implementation, such as data quality, privacy concerns, and skill gaps, proposing practical solutions. The article concludes by examining future trends like automated machine learning, edge computing, and explainable AI, emphasizing their potential to further revolutionize data-driven decision making.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-11
DOI
https://doi.org/10.5281/zenodo.13919211
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

HARNESSING THE POWER OF AI AND MACHINE LEARNING IN CLOUD DATA WAREHOUSING: TRANSFORMING DATA INTO INSIGHTS

Zenodo (CERN European Organization for Nuclear Research)
Big Data and Business Intelligence
article

HARNESSING THE POWER OF AI AND MACHINE LEARNING IN CLOUD DATA WAREHOUSING: TRANSFORMING DATA INTO INSIGHTS

article en

Abstract

This article explores the transformative integration of artificial intelligence (AI) and machine learning (ML) with cloud data warehousing, examining its profound impact on data management and analytics across industries. It outlines the key benefits of this integration, including enhanced data management, improved analytical capabilities, and generation of actionable insights. The article discusses industry-specific use cases in finance, healthcare, and retail, highlighting tangible improvements in areas such as risk assessment, patient care, and inventory optimization. Additionally, it addresses critical challenges in implementation, such as data quality, privacy concerns, and skill gaps, proposing practical solutions. The article concludes by examining future trends like automated machine learning, edge computing, and explainable AI, emphasizing their potential to further revolutionize data-driven decision making.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 41%
Big Data and Business Intelligence
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