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Advanced Data Modeling Techniques in Power BI for Enterprise Analytics

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  • Paul Praveen Kumar Ashok

Abstract

The rapid growth of enterprise data has intensified the need for advanced analytics solutions that are scalable, efficient, and adaptable. Microsoft Power BI has emerged as a leading platform, offering robust capabilities for data modeling that extend beyond traditional reporting. This article examines advanced modeling techniques including composite models, aggregations, calculation groups, and incremental refresh that enable organizations to handle complex, large-scale datasets while ensuring performance and governance. It also explores the integration of artificial intelligence within Power BI, such as AI-driven transformations and predictive analytics, to enhance data preparation and insight generation. Emphasis is placed on enterprise-scale considerations, including hybrid cloud architectures, real-time streaming data, and integration with platforms such as Azure Synapse and Databricks. Practical applications are illustrated through case studies in financial forecasting, supply chain optimization, and customer segmentation, demonstrating how sophisticated modeling approaches drive tangible business value. Challenges such as performance bottlenecks, compliance, and governance are addressed, along with best practices for sustainable deployment. The article concludes by highlighting emerging trends in semantic modeling, AI copilots, and the convergence of business intelligence with advanced analytics, underscoring Power BI’s evolving role in enterprise digital transformation.

Suggested Citation

  • Paul Praveen Kumar Ashok, 2020. "Advanced Data Modeling Techniques in Power BI for Enterprise Analytics," International Journal of Computing and Engineering, CARI Journals Limited, vol. 1(2), pages 32-42.
    • Handle: RePEc:bhx:ojijce:v:1:y:2020:i:2:p:32-42:id:3365
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    References listed on IDEAS

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    1. Max Kuhn & Kjell Johnson, 2013. "Applied Predictive Modeling," Springer Books, Springer, number 978-1-4614-6849-3, January.
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