Orclever Proceedings of Research and Development

Open-Source LLM Integrated Data Analysis Assistant with Tableau

Buşra Sabak

Metric Yazılım Danışmanlık

https://orcid.org/0009-0006-1912-358X

Erhan Alasar

Metric Yazılım Danışmanlık

https://orcid.org/0009-0005-9811-8764

DOI: https://doi.org/10.56038/oprd.v7i1.703

Keywords: Real Time Data Analysis, Large Language Models, Cloud-Native, Artificial Intelligence, Tableau Integrated

---

Abstract

In corporate data analysis processes, the ability for users to perform data querying and data analysis using natural language, without needing technical knowledge, has become a critical requirement, especially for mid and senior-level managers. This study proposes a solution that offers natural language interaction in TR/EN languages and meets internal data security requirements. The platform connects to Tableau data sources through open-source LLM integration and communicates with data sources published in Tableau via the VDS API to provide real-time analysis and predictions. The architecture also has the flexibility to be integrated with cloud-native AI services in the future. The solution, with its self-service ease of use, enables data analysts and decision-makers to obtain rapid insights without having to worry about technical details.

---

References

Acharjya, D. P., & Kauser, A. P. (2016). A Survey on Big Data Analytics: Challenges, OpenResearch Issues and Tools. International Journal of Advanced Computer Science and Applications, 7(2), 511-518. DOI: https://doi.org/10.14569/IJACSA.2016.070267

Alghamdi, N. A., & Al-Baity, H. H. (2022). Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence. Sensors, 22(20). DOI: https://doi.org/10.3390/s22208071

Gad-Elrab, A. A. (2021). Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value. E-Business - Higher Education and Intelligence Applications. içinde DOI: https://doi.org/10.5772/intechopen.97374

Guo, Y., Shi, D., Guo, M., Wu, Y., Chen, Q., & Cao, N. (2023). Talk2Data: A Natural Language Interface for Exploratory Visual Analysis via Question Decomposition. https://arxiv.org/abs/2107.14420 adresinden alındı DOI: https://doi.org/10.1145/3643894

Klisarova-Belcheva, S., Ilieva, G., & Yankova, T. (2017). Business Intelligence and Analytics – Contemporary System Model. Trakia Journal of Sciences, 15, 98-304,. doi:doi:10.15547/tjs.2017.s.01.053 DOI: https://doi.org/10.15547/tjs.2017.s.01.053

Morton, K., Bunker, R., Mackinley, J., Morton, R., & Stolte, C. (2012). Dynamic Workload Driven Data Integration in Tableau. SIGMOD'12. DOI: https://doi.org/10.1145/2213836.2213961

Pinheiro, J., Victorio, W., Nascimento, E., Seabra, A., Izquierdo, Y., García, G., . . . Casanova, M. (2023). On the Construction of Database Interfaces Based on Large Language Models. Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023), (s. 373-380). doi:10.5220/0012204000003584 DOI: https://doi.org/10.5220/0012204000003584

Shen, L., Shen, E., Luo, Y., Yang, X., Hu, X., Zhang, X., . . . Wang, J. (2021). Towards Natural Language Interfaces for Data Visualization: A Survey. IEEE Transactions on Visualization and Computer Graphics, 29, 3121-3144. DOI: https://doi.org/10.1109/TVCG.2022.3148007

Tableau Community. (2019). Sample - Superstore Sales (Excel). https://community.tableau.com/s/question/0D54T00000CWeX8SAL/sample-superstore-sales-excelxls adresinden alındı

Thanasas, G. L., & Kampiotis, G. (2024). The role of Big Data Analytics in Financial Decision-Making and Strategic Accounting. Technium Business and Management, 10, 17-33. DOI: https://doi.org/10.47577/business.v10i.11877

Zhang, J., Zhang, H., Chakravarti, R., Hu, Y., Ng, P., Katsifodimos, A., . . . Halevy, A. (2025). CoddLLM: Empowering Large Language Models for Data Analytics. https://arxiv.org/abs/2502.00329 adresinden alındı

M. J. Baeth and M. Aktas, “On the detection of information pollution and violation of copyrights in the social web,” Proc. IEEE SOCA, 2015. DOI: https://doi.org/10.1109/SOCA.2015.27

M. B. Çatalkaya, O. Kalıpsız, M. S. Aktaş, and U. O. Turgut, “Data feature selection methods on distributed big data processing platforms,” in Proc. UBMK, 2018. DOI: https://doi.org/10.1109/UBMK.2018.8566451

A. Mollaoğlu, G. Baltaoğlu, E. Çakır, and M. S. Aktaş, “Fraud detection on streaming customer behavior data with unsupervised learning methods,” in Proc. ICECCO, 2021. DOI: https://doi.org/10.1109/ICECCE52056.2021.9514152

B. Yildiz, “Efficient text classification with deep learning on imbalanced data improved with better distribution,” Turk. J. Sci. Technol., vol. 17, no. 1, pp. 89–98, 2022. DOI: https://doi.org/10.55525/tjst.1068940

D. Bakır, M. S. Aktaş, and B. Yıldız, “A model-based evaluation metric for question answering systems,” Int. J. Softw. Eng. Knowl. Eng., vol. 35, no. 2, pp. 243–262, 2025. DOI: https://doi.org/10.1142/S0218194025500032

M. S. Çiftlikçi, Y. Çakmak, T. A. Kalaycı, F. Abut, M. F. Akay, and M. Kızıldağ, “A New Large Language Model for Attribute Extraction in E-Commerce Product Categorization,” Electronics, vol. 14, no. 10, 1930, 2025. DOI: https://doi.org/10.3390/electronics14101930