International Journal of Science, Engineering and TechnologyUnlocking Power BI’s Potential: Mastering DAX For Dynamic Data Analysis And Complex Calculations
Authors: Sana Parveen
Abstract: Power BI, combined with the versatile Data Analysis Expressions (DAX) language, has emerged as a leading platform for transforming complex datasets into actionable business insights. This review explores the principles, methodologies, and best practices for mastering Power BI and DAX, emphasizing dynamic calculations, advanced data modeling, and optimized dashboard performance. It examines key components, including data connectivity, ETL processes, visualization frameworks, and performance tuning strategies that ensure responsive, scalable, and user-centric BI solutions. The article highlights advanced analytical techniques such as time intelligence, scenario analysis, and predictive modeling, illustrating how DAX enables complex, context-aware calculations. Industry-specific applications in finance, healthcare, and retail demonstrate practical use cases and the strategic value of data-driven decision-making. Challenges such as performance bottlenecks, large datasets, and user adoption barriers are addressed, alongside mitigation strategies involving efficient data structures, optimized DAX coding, and intuitive dashboard design. The review also presents emerging trends, including AI-assisted analytics, real-time streaming, cloud-native deployments, and natural language interaction, offering insights into the future trajectory of Power BI and enterprise BI solutions. By integrating technical mastery with strategic application, this article provides a comprehensive roadmap for developers, analysts, and decision-makers to leverage Power BI and DAX effectively, ensuring enhanced operational efficiency, informed decision-making, and sustainable competitive advantage.
DOI: http://doi.org/
Building Interactive Dashboards In Tableau: A Step-By-Step Guide For Beginners And Advanced Users
Authors: Harjit Singh
Abstract: The evolution of business intelligence (BI) has shifted the focus from static reporting to interactive, data-driven storytelling, enabling organizations to make timely and informed decisions. Tableau has emerged as a leading platform in this landscape, providing intuitive, self-service dashboards, advanced visualization capabilities, and an in-memory associative engine that facilitates rapid exploration of complex datasets. This review article presents a comprehensive examination of Tableau’s architecture, data integration strategies, visualization frameworks, and advanced features for creating compelling and actionable dashboards. Emphasis is placed on best practices for data modeling, dashboard design, performance optimization, and governance, ensuring efficient deployment and high user adoption across enterprises. Industry-specific applications in finance, healthcare, and retail are analyzed to demonstrate real-world impact, highlighting how Tableau enhances operational efficiency, risk management, and strategic planning. Additionally, challenges such as data quality, scalability, and user training are addressed alongside mitigation strategies. The article also explores emerging trends, including AI-assisted analytics, real-time data streaming, cloud-based deployments, and mobile accessibility, providing a forward-looking perspective on Tableau’s evolving capabilities. By combining technical insights with practical recommendations, this review serves as a valuable guide for BI developers, analysts, and decision-makers seeking to maximize the effectiveness of their Tableau implementations and harness data for strategic advantage.
DOI: https://doi.org/10.5281/zenodo.17276908
The Art Of ETL: A Comprehensive Guide To SQL Server Integration Services (SSIS) And Data Quality
Authors: Zubair Khan
Abstract: The growing complexity of enterprise data ecosystems has elevated the role of Extract, Transform, and Load (ETL) processes in ensuring reliable, timely, and high-quality information for decision-making. SQL Server Integration Services (SSIS), as Microsoft’s flagship ETL platform, provides a comprehensive framework for data integration, transformation, and quality management. This review explores the evolution of ETL, the architecture and core components of SSIS, and its practical applications across industries such as retail, healthcare, finance, and cloud-based environments. By examining best practices, data quality strategies, and governance frameworks, the study highlights how organizations can optimize SSIS packages for efficiency and reliability. Comparative analysis with competing platforms such as Informatica, Talend, and cloud-native services reveals both the strengths and limitations of SSIS, particularly in scalability and hybrid integration. Furthermore, future trends such as AI-driven automation, serverless ETL models, and cloud-native architectures are evaluated to understand the evolving role of SSIS in modern data ecosystems. This review concludes that while SSIS faces challenges in distributed and multi-cloud contexts, it remains a powerful and cost-effective solution for enterprises aligned with Microsoft technologies, capable of bridging legacy infrastructures with next-generation data platforms.
DOI: https://doi.org/10.5281/zenodo.17276968