Advanced Data Modeling Techniques in Power BI for Enterprise Analytics

Authors

  • Paul Praveen Kumar Ashok Houston Independent School District

DOI:

https://doi.org/10.47941/ijce.3365

Keywords:

Power BI, Data Modeling, Enterprise Analytics, Composite Models, Row-Level Security (RLS), Cloud Integration, Databricks

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.

Downloads

Download data is not yet available.

References

[1] M. Teixeira and A. I. Azevedo, “Self-service business intelligence: Current approaches, challenges and trends,” Procedia Computer Science, vol. 138, pp. 262–270, 2018.

[2] J. Larcker and B. Tayan, “Critical performance considerations for business intelligence reporting,” Journal of Business Analytics, vol. 1, no. 1, pp. 1–14, 2018.

[3] D. Loshin, Business Intelligence: The Savvy Manager’s Guide, 2nd ed. Burlington, MA: Morgan Kaufmann, 2013.

[4] C. Ballard, D. M. Farrell, A. Gupta, C. Mazuela, and S. Vohnik, Dimensional Modeling: In a Business Intelligence Environment. IBM Redbooks, 2012.

[5] R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed. Hoboken, NJ: John Wiley & Sons, 2013.

[6] A. Ferrari and M. Russo, The Definitive Guide to DAX: Business Intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI. Redmond, WA: Microsoft Press, 2015.

[7] C. Adamson, Star Schema: The Complete Reference. New York, NY: McGraw-Hill, 2010.

[8] A. Ferrari and M. Russo, Analyzing Data with Power BI and Power Pivot for Excel. Redmond, WA: Microsoft Press, 2017.

[9] K. Sivaraman, “Design patterns for enterprise-scale Power BI models,” SQL Server Pro Magazine, vol. 19, no. 4, pp. 44–53, 2018.

[10] Microsoft, “Row-level security (RLS) with Power BI,” Microsoft Docs, 2019. [Online]. Available: [https://docs.microsoft.com/en-us/power-bi/admin/service-security-rls]

[11] T. Davenport and R. Ronanki, “Artificial intelligence for the real world,” Harvard Business Review, vol. 96, no. 1, pp. 108–116, 2018.

[12] F. Provost and T. Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Sebastopol, CA: O’Reilly Media, 2013.

[13] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY: Springer, 2013.

[14] G. Bacciu, A. Micheli, and M. Sperduti, “Compositional generative mapping for tree-structured data Part II: Topographic mapping,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 12, pp. 2099–2111, 2012.

[15] W. H. Inmon, Building the Data Warehouse, 4th ed. Indianapolis, IN: Wiley Publishing, 2005.

[16] M. Armbrust et al., “Apache Spark: A unified engine for big data processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.

[17] P. Schreiner, “Real-time analytics: Concepts and business value,” Information Systems Management, vol. 34, no. 3, pp. 240–246, 2017.

[18] R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7th ed. Boston, MA: Pearson, 2016.

[19] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. Melbourne, Australia: OTexts, 2018.

[20] S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning, and Operation, 6th ed. Boston, MA: Pearson, 2016.

[21] L. Rokach and O. Maimon, Data Mining with Decision Trees: Theory and Applications, 2nd ed. Singapore: World Scientific, 2014.

[22] T. H. Davenport, Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Press, 2007.

[23] R. Kimball and J. Caserta, The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Hoboken, NJ: Wiley, 2004.

[24] D. Loshin, Enterprise Knowledge Management: The Data Quality Approach. San Francisco, CA: Morgan Kaufmann, 2001.

[25] G. Shanks, E. Tansley, and R. Weber, “Using ontology to validate conceptual models,” Communications of the ACM, vol. 46, no. 10, pp. 85–89, 2003.

[26] L. Bass, I. Weber, and L. Zhu, DevOps: A Software Architect’s Perspective. Boston, MA: Addison-Wesley, 2015.

[27] T. Erl, R. Khattak, and P. Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques. Upper Saddle River, NJ: Prentice Hall, 2016.

[28] S. Abiteboul, R. Hull, and V. Vianu, Foundations of Databases. Reading, MA: Addison-Wesley, 1995.

[29] D. Allemang and J. Hendler, Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, 2nd ed. Burlington, MA: Morgan Kaufmann, 2011.

[30] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008.

Downloads

Published

2020-02-27

How to Cite

Ashok, P. P. K. (2020). Advanced Data Modeling Techniques in Power BI for Enterprise Analytics. International Journal of Computing and Engineering, 1(2), 32–42. https://doi.org/10.47941/ijce.3365

Issue

Vol. 1 No. 2 (2020)

Section

Articles

License

Copyright (c) 2020 Paul Praveen Kumar Ashok

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.