The transformative impact of AI on data engineering, data science, and business intelligence

The advent of artificial intelligence is transforming business intelligence, reshaping the roles of data professionals, and offering unprecedented capabilities across the data lifecycle. This article examines how AI technologies are revolutionizing data engineering through automated pipeline construction, intelligent data quality management, and seamless data integration while simultaneously enhancing data science with automated feature engineering, democratized machine learning, and explainable decision support. Current trends in real-time analytics, cloud-native architectures, edge intelligence, and federated learning illustrate the evolving landscape. Despite these advancements, significant challenges persist in data governance, algorithmic bias, model explainability, and workforce transformation. By exploring both opportunities and limitations, the article provides a balanced perspective on how organizations can harness AI to elevate their business intelligence capabilities while addressing ethical and practical concerns.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-04-23
DOI
https://doi.org/10.30574/wjaets.2025.15.1.0365
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

The transformative impact of AI on data engineering, data science, and business intelligence

Anil D. Pathak
1 links
World Journal of Advanced Engineering Technology and Sciences
Big Data and Business Intelligence
article

The transformative impact of AI on data engineering, data science, and business intelligence

Anil D. Pathak
article en

Abstract

The advent of artificial intelligence is transforming business intelligence, reshaping the roles of data professionals, and offering unprecedented capabilities across the data lifecycle. This article examines how AI technologies are revolutionizing data engineering through automated pipeline construction, intelligent data quality management, and seamless data integration while simultaneously enhancing data science with automated feature engineering, democratized machine learning, and explainable decision support. Current trends in real-time analytics, cloud-native architectures, edge intelligence, and federated learning illustrate the evolving landscape. Despite these advancements, significant challenges persist in data governance, algorithmic bias, model explainability, and workforce transformation. By exploring both opportunities and limitations, the article provides a balanced perspective on how organizations can harness AI to elevate their business intelligence capabilities while addressing ethical and practical concerns.

World Journal of Advanced Engineering Technology and SciencesVol. 15(1)
Openalex Percentile: Top 87%
Big Data and Business Intelligence
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