Authors : Tejas Sriprasad

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/4uypsxpc

Scribd : https://tinyurl.com/44stbafv

DOI : https://doi.org/10.38124/ijisrt/26aug061

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The background of this paper will explore data governance and types of implementations of Data warehousing technologies in the Investment Banking Industry. Many implementations use the data warehousing technologies connected to the cloud in investment banking; our research will aim to set the frontiers of Data Governance and expand the horizons for Data Science Research. In different implementations and varying organization structures data warehousing, data science projects are in huge demand. There are many issues with data warehousing implementations, including high failure rates of data warehousing projects, trying to fix them in a way never done before, will be the secondary aim of this research paper. Accidental data redundancy, data quality issues, causing high failure rates, this research paper will look at possible solutions to alleviate this issue. There are different implementations of the warehouse which are highly successful, they include data lakes, data meshes, and data ponds. Data governance includes full data management and the way we manage both big data and analytics.

Keywords : Data Warehousing, Dimension Modeling, Data Governance, Data Warehouse Architecture Planning, Requirement Gathering,, Azure, Aws, Cloud Data, Lakehouse, Data Science, Data Analytics, Cloud Computing, Data Science Success, Investment Banking

References :

1. Adams, Karen., & Brace, I. (2006). An introduction to market & social research: planning & using research tools & techniques. Kogan Page.
2. Bilal, M., Sheikh, U., Raza, B., & Javaid, Q. (2016). Application of Data Warehouse in Real Life: State-of-the-art Survey from User Preferences’ Perspective.
3. International Journal of Advanced Computer Science and Applications, 7(4). https://doi.org/10.14569/ijacsa.2016.070455
4. Chandra, P., & Gupta, M. K. (2018). Comprehensive survey on data warehousing research. International Journal of Information Technology (Singapore), 10(2). https://doi.org/10.1007/s41870-017-0067-y
5. Dinesh, L., & Devi, K. G. (2024). An efficient hybrid optimization of ETL process in data warehouse of cloud architecture. Journal of Cloud Computing, 13(1). https://doi.org/10.1186/s13677-023-00571-y
6. Hwang, M. I., & Xu, H. (2005). A survey of data warehousing success issues. Business Intelligence Journal, 10(4).
7. (Citigroup)
8. Research Methods for Business Students. (n.d.). www.pearson.com/uk.
9. Saddad, E., El-Bastawissy, A., Mokhtar, H. M. O., & Hazman, M. (2020). Lake data warehouse architecture for big data solutions. International Journal of Advanced Computer Science and Applications, 11(8). https://doi.org/10.14569/IJACSA.2020.0110854
10. Wani, A. A., & Raina, B. L. (2019). Issues and handy solutions addressed at every stage in real time data warehousing, I.E. ETL (Extraction, transformation & loading). International Journal of Engineering and Advanced Technology, 8(5 Special Issue 3). https://doi.org/10.35940/ijeat.E1100.0785S319
11. R. J. Santos, J. Bernardino and M. Vieira, "A survey on data security in data warehousing: Issues, challenges and opportunities," 2011 IEEE EUROCON - International Conference on Computer as a Tool, Lisbon, Portugal, 2011, pp. 1-4, doi: 10.1109/EUROCON.2011.5929314.
12. Ponniah, P. (2011). Data warehousing fundamentals for IT Professionals, Second edition. In Data Warehousing Fundamentals for IT Professionals, Second Edition. https://doi.org/10.1002/9780470604137
13. Satyanarayana Reddy, G., Srinivasu, R., Poorna, M., Rao, C., & Rikkula, S. R. (2010). Data Warehousing, Data Mining, Olap and Oltp Technologies Are Essential Elements to Support Decision-Making Process in Industries. International Journal on Computer Science and Engineering, 02(09)
14. Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly: Management Information Systems, 25(1). https://doi.org/10.2307/3250957
15. Hendayun M, Yulianto E, Rusdi JF, Setiawan A, Ilman B (2021) Extracttransform load process in banking reporting system. MethodsX 8:101260
16. Farhan, M. S., Youssef, A., & Abdelhamid, L. (2024). A Model for Enhancing Unstructured Big Data Warehouse Execution Time. Big Data and Cognitive Computing, 8(2). https://doi.org/10.3390/bdcc8020017
17. Georgiev, A., & Valkanov, V. (2024). CUSTOM DATA QUALITY MECHANISM IN DATA WAREHOUSE FACILITATED BY DATA INTEGRITY CHECKS Mathematics and Education in Mathematics, 53. https://doi.org/10.55630/mem.2024.53.067-075