Comparing ATI, CCT and SCM in Power BI for Performance and Applications: An Analytical Approach
Aniket Alvekar *
SOCMS, Sandip University, Nashik, Maharashtra, India.
*Author to whom correspondence should be addressed.
Abstract
This research undertakes a comparative evaluation of three distinct approaches to time intelligence modeling within Microsoft Power BI: Auto-Time Intelligence, Custom Calendar Tables, and Imported Semantic Calendar Models. Drawing on a comprehensive dataset and leveraging Power BI’s Performance Analyzer, the study examines core time-based metrics such as Year-over-Year (YoY) and Month-over-Month (MoM) growth across a variety of visualizations. The results demonstrate that Custom Calendar Tables consistently outperform the other models in terms of visual responsiveness and processing efficiency. By offering a practical performance assessment alongside real-world use cases, this study provides valuable insights for business intelligence developers seeking optimized and scalable reporting solutions.
Keywords: Business intelligence, power BI, auto-time intelligence, custom calendar, semantic model