A Proposal: From Failure to Success for Data Warehousing in Investment Banking Projects

Tejas Sriprasad ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 08, 171-175

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 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:08:ijisrt26aug061

DOI: 10.38124/ijisrt/26aug061

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