A SYSTEMATIC REVIEW OF THE ROLE OF SQL AND EXCEL IN DATA-DRIVEN BUSINESS DECISION-MAKING FOR ASPIRING ANALYSTS

Abstract

This systematic review explores the integrated roles of Structured Query Language (SQL) and Microsoft Excel in enabling data-driven decision-making (DDDM) for aspiring analysts. As organizations increasingly rely on data-centric strategies to enhance operational efficiency, strategic planning, and organizational intelligence, the proficiency in SQL and Excel emerges as foundational to business analytics. The study followed the PRISMA 2020 guidelines to ensure methodological transparency and rigor. A total of 95 peer-reviewed articles published between 2000 and 2022 were identified, screened, and thematically synthesized. The reviewed literature encompasses academic research, case studies, and industry reports across multiple sectors, including finance, healthcare, education, and public administration. Findings from the review indicate that SQL is widely recognized for its backend querying capabilities, particularly in handling large structured datasets, supporting relational database systems, and facilitating efficient ETL workflows. Microsoft Excel is similarly lauded for its intuitive interface, extensive computational functions, and adaptability in visualization and modeling tasks. Importantly, the synergy between SQL and Excel especially through tools like Power Query and ODBC connections enables streamlined analytics workflows that enhance data accessibility and decision-making effectiveness across diverse user groups. In educational and professional training contexts, SQL and Excel are prominent components of curriculum and certification programs aimed at fostering data literacy and analytical thinking. However, a significant gap persists between academic instruction and industry expectations, especially in the application of integrated tool use. The study underscores the need for enhanced curriculum design, increased emphasis on experiential learning, and closer alignment with real-world data environments to equip aspiring analysts with the skills necessary for modern analytical roles. Overall, this review synthesizes critical insights into the technical, pedagogical, and strategic dimensions of SQL and Excel, providing a foundation for future research and curriculum development in analytical education and practice.