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Review Article Open Access October 30, 2022

Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration

Shakir Syed 1,*
1
Self-Service Data Science Program Leader, Cummins Inc, USA
Publihed in: Current Research in Public Health (Volume 2, Issue 1, 2022)
Page(s): 84-96
DOI: 10.31586/jaibd.2022.1157
Received
July 08, 2022
Revised
September 27, 2022
Accepted
October 22, 2022
Published
October 30, 2022
Keywords
Self-Service Business Intelligence (BI); Data Analytics; Autonomous Analytics; Data Visualization; Business Data Exploration; Intelligent Infrastructure; Scalable BI Systems; Hidden Insights; Large Datasets; Exploratory Data Analysis
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2022. Published by Scientific Publications
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Cite This Article

APA Style
Syed, S. (2022). Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration. Current Research in Public Health, 2(1), 84-96. https://doi.org/10.31586/jaibd.2022.1157
ACS Style
Syed, S. Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration. Current Research in Public Health 2022 2(1), 84-96. https://doi.org/10.31586/jaibd.2022.1157
Chicago/Turabian Style
Syed, Shakir. 2022. "Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration". Current Research in Public Health 2, no. 1: 84-96. https://doi.org/10.31586/jaibd.2022.1157
AMA Style
Syed S. Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration. Current Research in Public Health. 2022; 2(1):84-96. https://doi.org/10.31586/jaibd.2022.1157 
@Article{crph1157,
AUTHOR = {Syed, Shakir},
TITLE = {Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {84-96},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1157},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.1157},
ABSTRACT = {Self-service business intelligence (BI) platforms have become essential applications for exploring, analyzing, and visualizing business data in various domains. Here, we envisage that the business intelligence platform will perform automatic and autonomous data analytics with minimal to no user interaction. We aim to offer a data-driven, intelligent, and scalable infrastructure that amplifies the advantages of BI systems and discovers hidden and complex insights from very large business datasets, which a business analyst can miss during manual exploratory data analysis. Towards our future vision of autonomous analytics, we propose a collective machine learning model repository with an integration layer for user-defined analytical goals within the BI platform. The proposed architecture can effectively reduce the cognitive load on users for repetitive tasks, democratizing data science expertise across data workers and facilitating a less experienced user community to develop and use advanced machine learning and statistical algorithms.},
}
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AB  - Self-service business intelligence (BI) platforms have become essential applications for exploring, analyzing, and visualizing business data in various domains. Here, we envisage that the business intelligence platform will perform automatic and autonomous data analytics with minimal to no user interaction. We aim to offer a data-driven, intelligent, and scalable infrastructure that amplifies the advantages of BI systems and discovers hidden and complex insights from very large business datasets, which a business analyst can miss during manual exploratory data analysis. Towards our future vision of autonomous analytics, we propose a collective machine learning model repository with an integration layer for user-defined analytical goals within the BI platform. The proposed architecture can effectively reduce the cognitive load on users for repetitive tasks, democratizing data science expertise across data workers and facilitating a less experienced user community to develop and use advanced machine learning and statistical algorithms.
DO  - Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration
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