A Comparative Analysis on Various Business Intelligence (BI), Data Science and Data Analytics Tools

Sharma, Koustubh; Shetty, Aditya; Jain, Arnish; Dhanare, Ritesh

doi:10.1109/iccci50826.2021.9402226

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…AI algorithms, when combined with BI, enhance the ability to process large datasets, identify patterns, and predict future trends, thereby enabling businesses to make more informed decisions. Sharma et al (2021) provide a comparative analysis of various BI, data science, and data analytics tools, highlighting the increasing convergence of these technologies. The study underscores that while BI and data analytics have distinct origins and purposes, their integration has led to a more comprehensive approach to data-driven decision-making.…”

Section: The Synergy Between Ai Data Analytics and Business Intelligencementioning

confidence: 99%

“…This section delves into the methodologies and approaches utilized in synthesizing and analyzing data within the context of AI-driven BI, drawing upon key insights from recent scholarly works. Sharma et al (2021) provide a comprehensive comparative analysis of various BI, Data Science, and Data Analytics tools, highlighting the importance of selecting appropriate tools for effective data synthesis and analysis. Their study emphasizes the need for tools that can not only handle large volumes of data but also provide meaningful insights that are actionable in a business context.…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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This paper delves into the transformative role of Artificial Intelligence (AI) and Data Analytics in the realm of Business Intelligence (BI), marking a significant shift in the landscape of business decision-making and strategic planning. The study's purpose was to comprehensively explore the evolution of BI, underscored by the integration of AI and advanced data analytics, and to project the future trajectory of these technologies within the business context. Adopting a systematic literature review as its methodology, the study meticulously analyzed a wide array of scholarly articles and industry reports. This approach facilitated a deep understanding of the historical development of BI, the current synergy between AI, Data Analytics, and BI, and the emerging trends shaping their future. The inclusion and exclusion criteria for sources were rigorously applied to ensure the relevance and quality of the information gathered. The findings of the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes. Conclusively, the study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations. It recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries. The study's classical and engaging tone aims to captivate and inform a diverse audience, from academic researchers to industry practitioners. Keywords: Artificial Intelligence, Business Intelligence, Data Analytics, Predictive Analytics.

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Section: The Synergy Between Ai Data Analytics and Business Intelligencementioning

confidence: 99%

Section: Data Synthesis and Analysismentioning

confidence: 99%