Creating Scalable Semantic Data Models with Tableau and Power BI

Authors

  • Niranjan Reddy Rachamala

Keywords:

improvements, Tableau, automated, semantic

Abstract

Organizations depend more on business intelligence (BI) tools to process growing and complex data sets, so needing scaled semantic data models is now essential. This research explores the use of both Tableau and Power BI in developing semantic layers that increase data consistency, make the data easier to use and enhance analytics. This research analyzes data preparation, modeling, integration approaches and performance metrics to see how flexible and scalable each tool is, as well as how easy they are for users. Current developments, including Auto-BI automation, integration via ontology and semantic binning, are reviewed to help us understand the present situation. The study has found that Power BI delivers better options for modeling and technical requirements, but Tableau is superior in interactive features and visual meaning. The final part of this paper discusses potential future improvements by making the semantic model more automated, integrating AI in BI systems and increasing the availability of semantically enriched data sources.

DOI: https://doi.org/10.17762/ijisae.v12i23s.7784

Downloads

Download data is not yet available.

References

Carlisle, S., 2018. Software: Tableau and microsoft power bi. Technology| Architecture+ Design, 2(2), pp.256-259.

Izang, A.A., Goga, N., Kuyoro, S.O., Alao, O.D., Omotunde, A.A. and Adio, A.K., 2019. Scalable Data Analytics Market Basket Model for Transactional Data Streams. International Journal of Advanced Computer Science and Applications, 10(1), pp.1-10.

Lee, J., Wei, T. and Mukhiya, S.K., 2018. Hands-On Big Data Modeling: Effective database design techniques for data architects and business intelligence professionals. Packt Publishing Ltd.

Mei, H., Guan, H., Xin, C., Wen, X. and Chen, W., 2020. Datav: Data visualization on large high-resolution displays. Visual Informatics, 4(3), pp.12-23.

Mishra, A., 2020. The Role of Data Visualization Tools in Real-Time Reporting: Comparing Tableau, Power BI, and Qlik Sense. IJSAT-International Journal on Science and Technology, 11(3).

Moens, P., Bracke, V., Soete, C., Vanden Hautte, S., Nieves Avendano, D., Ooijevaar, T., Devos, S., Volckaert, B. and Van Hoecke, S., 2020. Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications. Sensors, 20(15), p.4308.

Motamedi Nia, S., 2019. Visualizing Business Production Performance (Bachelor's thesis, University of Twente).

Oppermann, M., Kincaid, R. and Munzner, T., 2020. VizCommender: Computing text-based similarity in visualization repositories for content-based recommendations. IEEE Transactions on Visualization and Computer Graphics, 27(2), pp.495-505.

Petito, M., Fallucchi, F. and Luca, E.W.D., 2020. Semantic architectures and dashboard creation processes within the data and analytics framework. International Journal of Metadata, Semantics and Ontologies, 14(1), pp.1-15.

Powell, B., 2018. Mastering Microsoft Power BI: Expert techniques for effective data analytics and business intelligence. Packt Publishing Ltd.

Qin, X., Luo, Y., Tang, N. and Li, G., 2020. Making data visualization more efficient and effective: a survey. The VLDB Journal, 29(1), pp.93-117.

Rafif, M.F., 2019. Comparison study of the development of Self-Service Business Intelligence between Power BI and Tableau desktop (Case study: Microsoft Adventure Works).

Ramesh, S. and Henderson, A., 2018. A semantic data model: Meaning making from data structures in the SQL server. Journal of Information Systems Engineering and Business Intelligence, 4(2), pp.106-115.

Richardson, J., Sallam, R., Schlegel, K., Kronz, A. and Sun, J., 2020. Magic quadrant for analytics and business intelligence platforms. Gartner ID G00386610, pp.00041-5.

Vidojevic, B., 2019. Analysis and design of a semantic modeling language to describe public data sources. Master's thesis, Technischen Universit, Munchen.

Zhang, L., Chen, F. and Wei, W., 2020. A foundation course in business analytics: Design and implementation at two universities. Journal of Information Systems Education, 31(4), pp.244-259.

Downloads

Published

30.11.2024

How to Cite

Niranjan Reddy Rachamala. (2024). Creating Scalable Semantic Data Models with Tableau and Power BI . International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 3564 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7784

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.