Conversational Analytics Using LLMs: Transforming Enterprise Data Consumption through Natural Language Interfaces
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I5P108Keywords:
Conversational Analytics, Large Language Models, Natural Language Interfaces, Enterprise Data Systems, Artificial Intelligence, Business Intelligence, Natural Language Processing, Data Democratization, Enterprise Analytics, Intelligent Data InterfacesAbstract
Enterprise data ecosystems are expanding rapidly, creating new opportunities for organizations to derive insights for strategic decision-making. However, many business users struggle to interact with complex data infrastructures because traditional analytics tools require technical expertise such as SQL knowledge and the ability to interpret complex dashboards. This limitation restricts effective data democratization within organizations. Conversational analytics has emerged as a solution by enabling users to interact with enterprise data through natural language interfaces powered by large language models (LLMs). These systems translate user queries into structured database operations, allowing non-technical stakeholders to easily access and analyze data. By supporting interactive exploration, visualization requests, and contextual insights through dialogue, conversational analytics enhances data accessibility, collaboration, and decision-making agility. Despite its advantages, challenges such as query accuracy, model hallucination, data security, and system scalability must be addressed. This study explores the architecture and implementation of LLM-based conversational analytics systems for enterprise data consumption and evaluates their performance using metrics such as query accuracy, response latency, and user engagement. The findings suggest that conversational analytics significantly improves data accessibility and usability compared to traditional dashboard-based systems, making it a promising approach for future enterprise intelligence platforms.
References
[1] Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons.
[2] Howson, C. (2013). Successful business intelligence: Unlock the value of BI & big data. McGraw-Hill Education Group.
[3] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
[4] Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
[5] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[6] Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[7] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).
[8] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
[9] Gao, J., Galley, M., & Li, L. (2018, June). Neural approaches to conversational AI. In The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 1371-1374).
[10] Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE intelligent systems, 24(2), 8-12.
[11] Lee, I. (2019). The Internet of Things for enterprises: An ecosystem, architecture, and IoT service business model. Internet of things, 7, 100078.
[12] Popescu, A. M., Etzioni, O., & Kautz, H. (2003, January). Towards a theory of natural language interfaces to databases. In Proceedings of the 8th international conference on Intelligent user interfaces (pp. 149-157).
[13] Khoje, M. (2024, February). Navigating data privacy and analytics: the role of large language models in masking conversational data in data platforms. In 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-5). IEEE.
[14] Affolter, K., Stockinger, K., & Bernstein, A. (2019). A comparative survey of recent natural language interfaces for databases. The VLDB Journal, 28(5), 793-819.
[15] Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data Big Analytics: Emerging Business Intelligence and Analytic Trends for Todays Businesses. John Wiley.
[16] Powers, D. M., & Turk, C. C. (2012). Machine learning of natural language. Springer Science & Business Media.
[17] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Rush, A. M. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations (pp. 38-45).
[18] Symeonaki, E., Arvanitis, K., Piromalis, D., & Papoutsidakis, M. (2019, August). Conversational user interface integration in controlling IoT devices applied to smart agriculture: Analysis of a chatbot system design. In Proceedings of SAI Intelligent Systems Conference (pp. 1071-1088). Cham: Springer International Publishing.
[19] Muntala, P. S. R. P. (2022). Natural Language Querying in Oracle Fusion Analytics: A Step toward Conversational BI. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 81-89.
[20] Ghavami, P. (2019). Big data analytics methods: analytics techniques in data mining, deep learning and natural language processing. Walter de Gruyter GmbH & Co KG.
[21] O’Leary, D. E. (2022). Massive data language models and conversational artificial intelligence: Emerging issues. Intelligent Systems in Accounting, Finance and Management, 29(3), 182-198.
