THE EVOLUTION OF DATA ANALYSIS TOOLS: FROM SPREADSHEETS TO ARTIFICIAL INTELLIGENCE AGENTS
DOI:
https://doi.org/10.30890/2567-5273.2025-42-03-055Keywords:
data analysis evolution, analytical paradigms, electronic tables, statistical calculations; agents of artificial intelligence, methodological accuracyAbstract
The evolution of data analysis tools signals a deeper transformation in the foundations of scientific research. This article systematically analyzes the transition between three analytical paradigms - spreadsheets, statistical calculations, and agents basReferences
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). New York, NY, USA: Association for Computing Machinery, pp. 610–623. doi: 10.1145/3442188.3445922.
Blei, D. M. and Smyth, P. (2017). Science and data science. Proceedings of the National Academy of Sciences of the United States of America, 114(33), pp. 8689–8692. doi: 10.1073/pnas.1702076114.
Bommasani, R., Hudson, D.A., Ross, A. et al. (2022) On the Opportunities and Risks of Foundation Models. Working paper, arXiv:2108.07258. Available at: https://arxiv.org/abs/2108.07258 (Accessed: 11 December 2025).
Borji, A. (2023). A Categorical Archive of ChatGPT Failures. Research Square. Available at: https://doi.org/10.21203/rs.3.rs-2895792/v1.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Amodei, D., Brown, A., Clark, J., Askell, S., Chen, S., Child, R., Ziegler, D., Wu, J., Winter, C., Tittel, S., Poli, J., and Radford, A. (2020). Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). doi: 10.48550/arXiv.2005.14165.
Chambers, J.M. (1998) Programming with Data: A Guide to the S Language. New York: Springer.
Gandomi, A. and Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp. 137–144.
Grossman, T. (2007) 'Spreadsheet Engineering: A Research Framework', From the Proceedings of the European Spreadsheet Risks Interest Group Symposium, Cardiff, Wales, July 2002. Available at: https://doi.org/10.48550/arXiv.0711.0538 (Accessed: 11 December 2025).
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y.J., Madotto, A. and Fung, P. (2023) Survey of Hallucination in Natural Language Generation, ACM Computing Surveys, 55(12), pp. 1–38. Available at: https://doi.org/10.1145/3571730 (Accessed: 11 December 2025).
Kitchin, R. (2014). Big Data, New Epistemologies and Paradigm Shift. Big Data & Society, 1, pp. 1–12. doi: 10.1177/2053951714528481.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. Stamford, CT: META Group.
McKinney, W. (2010). Data Structures for Statistical Computing in Python. In: Proceedings of the 9th Python in Science Conference (SciPy 2010). Austin, Texas: SciPy, pp. 56–61. doi: 10.25080/Majora-92bf1922-00a.
Nardi, B. and Miller, J. (1990) 'The spreadsheet interface: A basis for end-user programming', Communications of the ACM, 33(12), pp. 72–81.
Panko, R. (2005). What We Know About Spreadsheet Errors. Journal of End User Computing, 10. doi: 10.4018/joeuc.1998040102.
Shapin, S. (2018). A Social History of Truth: Civility and Science in Seventeenth-Century England. Chicago: University of Chicago Press.
Suresh, H. and Guttag, J. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). New York, NY, USA: Association for Computing Machinery, pp. 1–13. doi: 10.1145/3442188.3445924.
Tumanov, O. (2025a). Large Language Models: A New Paradigm for Data Analysis. Business Inform, 9, pp. 106–113. doi: 10.32983/2222-4459-2025-9-106-113.
Tumanov, O. (2025b). The Unseen Data: A Statistical and Engineering Perspective on Biases in Large Language Models. SWorld Journal, 33(1), pp. 178–183. doi: 10.30888/2663-5712.2025-33-01-078.
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics With S. New York: Springer. doi: 10.1007/b97626.
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