Leveraging AI to Transform Data Analysis: A Practical Guide

The revolution of data analysis through artificial intelligence is examined in this thorough article, which also looks at how businesses may use AI technologies to improve their analytical and decision-making skills. From data cleansing and preparation to predictive analysis and automated insight production, the article explores the development of AI-powered analytics. It offers thorough insights into best practices, implementation methodologies, and the vital balance between AI automation and human expertise. The article looks at practical applications across a range of industries, showing notable gains in operational results, accuracy, and efficiency. The significance of data quality management, organized implementation frameworks, and the creation of models for human-AI collaboration are all given particular focus.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-12-10
DOI
https://doi.org/10.36948/ijfmr.2024.v06i06.32568
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging AI to Transform Data Analysis: A Practical Guide

Deepti Bitra -
International Journal For Multidisciplinary Research
Big Data and Business Intelligence
article

Leveraging AI to Transform Data Analysis: A Practical Guide

Deepti Bitra -
article en

Abstract

The revolution of data analysis through artificial intelligence is examined in this thorough article, which also looks at how businesses may use AI technologies to improve their analytical and decision-making skills. From data cleansing and preparation to predictive analysis and automated insight production, the article explores the development of AI-powered analytics. It offers thorough insights into best practices, implementation methodologies, and the vital balance between AI automation and human expertise. The article looks at practical applications across a range of industries, showing notable gains in operational results, accuracy, and efficiency. The significance of data quality management, organized implementation frameworks, and the creation of models for human-AI collaboration are all given particular focus.

International Journal For Multidisciplinary ResearchVol. 6(6)
Openalex Percentile: Top 65%
Big Data and Business Intelligence
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