Title: Exploring the data analytical capabilities of generative AI tools ChatGPT and Google Bard (Gemini): a comparative analysis of GenAI tools with excel and python

Authors: Nikhat Afshan; Vikram Chandramouli Rayadurgam; Angappa Gunasekaran; Girish H. Subramanian

Addresses: Department of Operations and IT, IBS, Hyderabad (A Constituent of ICFAI Foundation for Higher Education), Shankarapally Road, Telangana, 501203, India ' IBS, Hyderabad (A Constituent of ICFAI Foundation for Higher Education), Shankarapally Road, Telangana, 501203, India ' School of Business Administration, E355 Olmsted Building, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057-4898, USA ' School of Business Administration, E355 Olmsted Building, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057-4898, USA

Abstract: ChatGPT and Google Bard (now renamed as Google Gemini), the latest iterations of real-time generative artificial intelligence (GenAI), are being extensively used across discipline including education. There has been growing interest amongst academics to integrate GenAI into teaching to create personalised learning experience for students. Though the recent versions of GenAI have been designed to tackle complex natural language and data analytical problems, concerns have been raised regarding the reliability of the outputs generated by these GenAI tools. This paper tries to explore the data analytical capabilities of ChatGPT 4.0 and Bard and understand its suitability to teach data analytics courses. The study conducts and compares three statistical analyses viz. regression analysis, classification analysis and linear programming problem using GenAI tools (ChatGPT 4 and Google Bard) and traditional software (Python and Excel). The study discusses the nuances of using these tools effectively by students, educators, and practitioners.

Keywords: ChatGPT data analytics; generative AI; Google Bard; forecasting prediction; transportation problem; systematic literature review.

DOI: 10.1504/IJMDM.2026.150375

International Journal of Management and Decision Making, 2026 Vol.25 No.1, pp.1 - 20

Received: 07 Dec 2024
Accepted: 23 Jan 2025
Published online: 12 Dec 2025 *

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