International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Design and Implementation of a Validated End-to-End Business Intelligence Framework for Transactional Data Analysis Using SQL and Power BI |
|---|---|
| ABSTRACT | In the era of data-driven decision-making, organizations generate large volumes of transactional data through daily operations. However, raw data stored in relational databases often lacks structured validation, transformation, and analytical modeling required for strategic insights. Many existing Business Intelligence (BI) implementations focus primarily on visualization while neglecting backend data verification and cross-tool accuracy, leading to inconsistencies in reported metrics and unreliable decision support. This research proposes and implements a validated end-to-end Business Intelligence framework that integrates Structured Query Language (SQL) for backend data aggregation and verification with Power BI for dynamic data modeling and interactive visualization. The system introduces a two-way validation architecture where Key Performance Indicators (KPIs) are first computed and verified at the database level before being modeled using Data Analysis Expressions (DAX) in the reporting layer. The framework addresses common analytical challenges such as integer division errors, divide-by-zero exceptions, incorrect chronological sorting, filter-context miscalculations, and data transformation inconsistencies. It also incorporates structured ETL processes and dynamic KPI modeling to ensure scalable and accurate performance reporting. The proposed system demonstrates how raw transactional datasets can be transformed into a centralized, interactive decision-support dashboard capable of identifying temporal trends, performance outliers, and operational inefficiencies. The research contributes a replicable BI architecture that ensures data integrity, analytical precision, and strategic visibility across diverse organizational domains. |
| AUTHOR | SHAHIL PRASAD, NIRALI BHALIYA Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujrat, India Assistant Professor, Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujrat, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404012 |
| pdf/12_Design and Implementation of a Validated End-to-End Business Intelligence Framework for Transactional Data Analysis Using SQL and Power BI.pdf | |
| KEYWORDS | |
| References | [1] M. Chen, S. Mao, and Y. Liu, “Big Data: A Survey,” Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, 2014. [2] R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed. Hoboken, NJ, USA: Wiley, 2013. [3] C. Howson, Successful Business Intelligence: Unlock the Value of BI & Big Data, 2nd ed. New York, NY, USA: McGraw-Hill, 2013. [4] B. Wixom and H. Watson, “An Empirical Investigation of the Factors Affecting Data Warehousing Success,” MIS Quarterly, vol. 25, no. 1, pp. 17–41, 2001. [5] A. Silberschatz, H. F. Korth, and S. Sudarshan, Database System Concepts, 6th ed. New York, NY, USA: McGraw-Hill, 2011. [6] M. Russo and A. Ferrari, The Definitive Guide to DAX: Business Intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel, 2nd ed. Redmond, WA, USA: Microsoft Press, 2019. [7] C. Webb, “Understanding DAX Query Plans,” Business Intelligence Journal, vol. 18, no. 3, pp. 45–52, 2018. [8] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2011. [9] W. H. Inmon, Building the Data Warehouse, 4th ed. Indianapolis, IN, USA: Wiley, 2005. [10] E. F. Codd, S. B. Codd, and C. T. Salley, “Providing OLAP to User-Analysts: An IT Mandate,” Codd and Date Report, 1993. [11] T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning, Boston, MA, USA: Harvard Business School Press, 2007. [12] A. McAfee and E. Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review, vol. 90, no. 10, pp. 60–68, 2012. [13] H. Watson, “Tutorial: Business Intelligence — Past, Present, and Future,” Communications of the Association for Information Systems, vol. 25, no. 1, pp. 487–510, 2009. [14] P. Rob and C. Coronel, Database Systems: Design, Implementation, and Management, 10th ed. Boston, MA, USA: Cengage Learning, 2014. [15] T. Lahdenmaki and M. Leach, Relational Database Index Design and the Optimizers, Hoboken, NJ, USA: Wiley, 2005. [16] R. Kimball, “The Data Warehouse ETL Toolkit,” Wiley Publishing, 2004. [17] S. Few, Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Burlingame, CA, USA: Analytics Press, 2013. [18] A. Ferrari and M. Russo, “Optimizing DAX Calculations in BI Models,” Microsoft BI Whitepaper, 2017. [19] P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Boston, MA, USA: Pearson, 2006. [20] G. Shanks and P. Bekmamedova, “Achieving Benefits from Data Warehouses: A Practical Perspective,” Journal of Decision Systems, vol. 21, no. 1, pp. 1–17, 2012. |
