Building Self-Service BI in the Cloud with AI Integration: Power BI and Snowflake
DOI:
https://doi.org/10.63282/3050-9246/ICRTCSIT-135Keywords:
Self-Service BI, Security and Governance, Microsoft Power BI, Snowflake, Performance OptimizationAbstract
Organizations need suitable platforms for enabling insights on time with democratized access and promoting AI(Artificial Intelligence) supported decisions. Self-service BI (Business Intelligence) is emerging as a strategic element for encouraging business users to verify data distinctively while ascertaining high-level security and governance. Migrating into a cloud platform and processing by integrating with AI has transformed the ways that BI domains are created, deployed, and utilized. This paper involves studying about unification of Microsoft Power BI and Snowflake as a basis for developing a cloud-motivated self-facilitating BI ecosystem. Power BI is effective in conducting intuitive analytics and AI-motivated visualizing capabilities. Snowflake supports highly scalable cloud-native platforms, unifying storage and computing with organizational-level governance. This combination of tools and cloud creates a synergistic environment for pursuits to deliver performance and intelligence securely. Recent developments, including performance optimization mechanisms, are discussed in findings for fostering agile operations and addressing information technology(IT) bottlenecks through AI integration
Downloads
References
[1] S. Dashora, N. Gupta, M. P. S. Tanwar, A. Singh and S. Kaur, "Cloud-based Data Analytics for Business Intelligence," International Journal for Research in Applied Science & Engineering Technology, vol. 11, no. 11, pp. 2758-2765, 2023.
[2] M. Maghsoudi and N. Nezafati, "Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach," Telematics and Informatics Reports, vol. 11, no. 1, pp. 1-10, 2023.
[3] E. Y. Lai, Y. He and S. Chaudhuri, "Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence," Computer Science, vol. 2025, no. 1, pp. 1-10, 2025.
[4] W. k. Akram, "Revolutionizing Business Intelligence Through Cloud Computing, AI/ML, and Snowflake Database Optimization," ResearchGate, vol. 2020, no. 1, pp. 1-10, 2020.
[5] S. Syed, "Towards Autonomous Analytics: The Evolution of Self-Service BI Platforms with Machine Learning Integration," Journal of Artificial Intelligence and Big Data, vol. 2, no. 1, pp. 84-96, 2024.
[6] S. R. Karanam, "Advanced Analytics with Snowflake and Power BI," International Scientific Journal of Engineering and Management, vol. 3, no. 9, pp. 1-14, 2024.
[7] J. Forero, S. Chintala and N. Sehrawat, "Cortex Analyst: Paving the Way to Self-Service Analytics with AI," Snowflake, 15 August 2024. [Online]. Available: https://www.snowflake.com/en/blog/cortex-analyst-ai-self-service-analytics/. [Accessed 19 August 2025].
[8] J. Roering, "Best Practices for Delivering AI-Ready Data Products Using Snowflake Internal Marketplace," Snowflake, 31 July 2025. [Online]. Available: https://www.snowflake.com/en/blog/ai-ready-data-products-snowflake-internal-marketplace/. [Accessed 19 August 2025].
[9] P. Turley, "Delivering Enterprise and Self-service Power BI with Microsoft Fabric," Microsoft, 15 January 2025. [Online]. Available: https://sqlserverbi.blog/2025/01/15/delivering-enterprise-and-self-service-power-bi-with-microsoft-fabric/. [Accessed 19 August 2025].
[10] P. Turley, "Preparing Power BI and Fabric for AI & Copilot," Microsoft, 31 March 2025. [Online]. Available: https://sqlserverbi.blog/2025/03/31/preparing-power-bi-and-fabric-for-ai-copilot/. [Accessed 19 August 2025].
[11] S. Dandolu, "Cloud-Native Architecture for AI Data Platforms: A Snowflake Implementation Case Study," World Journal of Advanced Engineering Technology and Sciences, vol. 15, no. 3, pp. 475-485, 2025.
[12] K. I. Mohammed, "Data Warehouse Design and Implementation Based on Star Schema vs. Snowflake Schema," International Journal of Academic Research in Business and Social Sciences, vol. 9, no. 14, pp. 1-10, 2019.
[13] K. Singh, "Snowflake Architecture for Optimized Data Warehousing in Cloud Environments," Integrated Journal for Research in Arts and Humanities, vol. 4, no. 6, pp. 616-628, 2025.
[14] W. Ahmed, "Cyber Security in IoT-Based Cloud Computing: A Comprehensive Survey," MDPI Electronics, vol. 11, no. 1, pp. 1-10, 2021.
[15] K. Y. Chan, B. Abu-Salih, R. Qaddoura, A. M. A. Zoubi and V. Palade, "Deep neural networks in the cloud: Review, applications, challenges and research directions," Neurocomputing, vol. 545, no. 1, pp. 1-10, 2023.
[16] J. R. M. Thason and A. Jain, "Democratizing Data Insights with AI-Powered Report Generation in Power BI: Transforming Business Intelligence and Decision-Making," International Journal of All Research Education & Scientific Methods, vol. 13, no. 2, pp. 2455-6211, 2025.
[17] M. Lubis, M. F. Safitra, H. Fakhrurroja and A. N. Muttaqin, "Guarding Our Vital Systems: A Metric for Critical Infrastructure Cyber Resilience," MDPI Sensors, vol. 25, no. 15, pp. 1-10, 2025.
[18] Snowflake Inc, "https://www.snowflake.com/thankyou/attaining-data-driven-insights-with-snowflake-and-microsoft-power-bi/," Snowflake Inc, Online, 2025.
[19] S. Gershkovich and K. Graziano, "Data Modeling with Snowflake: A practical guide to accelerating Snowflake development using universal data modeling techniques," Packt Publishing, vol. 2023, pp. 1-10, 2023.
[20] M. Rodríguez, S. Saeed and M. Ade, "Snowflake-Powered Data Strategies: From Raw Data to Actionable Insights," Journal of Machine to Machine Communications, vol. 2025, no. 1, pp. 1-10, 2025.
[21] S. M. F. Azim, S. M. Rahman, N. Alam and T. Sultana, "Engaging Customers Towards Mobile Application Brand Attachment and Advocacy: Multigroup Analysis of Generational Differences," Journal of Applied Business and Economics, vol. 26, no. 3, pp. 1-10, 2024.
[22] D. Raychowdhuri, "Revolutionizing Business Intelligence: An AI-Driven Approach to Automated Insights," International Journal of Scientific Research in Computer Science Engineering and Information Technology, vol. 11, no. 2, pp. 1011-1021, 2025.
[23] P. Jorzik, S. Klein and D. K. Kanbach, "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, vol. 182, no. 18, pp. 1-10, 2024.
[24] Amrish Solanki, Kshitiz Jain, Shrikaa Jadiga, "Building a Data-Driven Culture: Empowering Organizations with Business Intelligence," International Journal of Computer Trends and Technology (IJCTT), vol. 72, no. 2, pp. 46-55, 2024. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V72I2P109
[25] Sharma, V. K. (2025). Cloud Computing & IoT: 5G Focused IoT with Cloud Solutions. International Journal of AI, BigData, Computational and Management Studies, 6(3), 21-25. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P103
[26] Reddy, R. P. (2025). Zero Trust Architectures in Modern Enterprises: Principles, Implementation Challenges, and Best Practices. International Journal of Computer Trends and Technology, 73(6), 48-57.
[27] Gunda, S. K. (2025). Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations. In B. Ben Youssef & M. Ben Ismail (Eds.), Integrating Machine Learning Into HPC-Based Simulations and Analytics (pp. 229-252). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3795-7.ch009
[28] M. Kommineni, S. Panyaram, S. Banala, G. C. Vegineni, M. Hullurappa and S. K. Sehrawat, "Optimizing Processes and Insights: the Role of Ai Architecture in Corporate Data Management," 2025 International Conference on Data Science and Business Systems (ICDSBS), Chennai, India, 2025, pp. 1-7, doi: 10.1109/ICDSBS63635.2025.11031505.
