International Journal of Science, Engineering and TechnologyAuthors: Armaan Yusuf
Abstract: Business Intelligence (BI) has become a cornerstone of modern organizations, enabling data-driven decision-making across industries. QlikView, a leading BI platform, offers a powerful combination of interactive dashboards, associative in-memory data models, and advanced ETL capabilities. This review article provides a practical guide to QlikView development and data modeling, focusing on best practices, performance optimization, and actionable insights. It explores the QlikView architecture, including its associative model and visualization features, and emphasizes the importance of effective data modeling through star and snowflake schemas, synthetic key management, and optimized load scripts. The article details the development lifecycle, from requirement gathering and ETL implementation to dashboard design, testing, and validation. Performance optimization techniques, such as data model refinement, expression tuning, and scalability strategies, are discussed to ensure responsive and efficient dashboards. Advanced features, including set analysis, variables, triggers, and integration with external tools like R and Python, enable sophisticated analytics and predictive insights. Industry case studies in finance, healthcare, and retail illustrate practical applications and the impact of QlikView on operational efficiency and strategic decision-making. The review also addresses common challenges, mitigation strategies, and emerging trends such as AI-driven analytics, cloud deployment, real-time dashboards, and natural language querying. By adhering to best practices and leveraging advanced features, organizations can maximize the value of QlikView, transforming raw data into actionable intelligence and fostering a culture of informed, proactive decision-making.
DOI: https://doi.org/10.5281/zenodo.17277809
