International Journal of Science, Engineering and TechnologyAuthors: Imran Saif
Abstract: Advanced data modeling in Power BI is critical for enabling high-performance, scalable, and accurate business intelligence. This review explores best practices for designing efficient data models, optimizing query performance, and maintaining data integrity across complex datasets. Key strategies include schema optimization using star and snowflake models, managing relationships and cardinality, leveraging calculation groups, hierarchies, and incremental refresh, and integrating with SQL databases and cloud platforms. The review also highlights techniques for query folding, VertiPaq optimization, and hybrid architectures that combine batch and real-time data. Case studies from finance, retail, healthcare, and logistics demonstrate practical applications and lessons learned in enterprise deployments. Challenges such as high-cardinality columns, complex DAX calculations, and organizational skill gaps are discussed alongside emerging trends in AI-driven modeling, automated optimization, and self-service governance. By following these best practices, organizations can build Power BI models that deliver actionable insights, ensure data accuracy, and support timely decision-making in enterprise environments.
DOI: http://doi.org/
