Data Warehousing

Edward H K Ng

Chapter 6 in Risk Analytics:From Concept to Deployment, 2021, pp 69-78 from World Scientific Publishing Co. Pte. Ltd.

Abstract: It is almost impossible to support analytics directly from operational or legacy systems. Organizations that have tried quickly face issues too challenging even for professional IT service providers. There is increasing recognition of this matter, but data warehousing remains a hit and miss undertaking in most places.The criticality of data warehousing has been recognized by the creation of cloud-based data warehouses. That is a boon to many types of industries, but for banks, it may not make a material difference. In most regulatory regimes, banks are required to keep data within their own premises. Even data centers are disallowed except for archived data. Creating a fully owned and controlled data warehouse is not an option.In one bank, around $30m was spent on developing a data warehouse by the IT associate company over 5 years. When the project was completed, not a single piece of datum could be extracted and used for analysis. The bank CEO ordered that this blunder be kept a secret to avoid embarrassment. In reality, such outcomes are fairly common. Knowing the difference between operational and analytical data can help reduce the frustrations often encountered.

Keywords: Risk; Modeling; Basel II; Quantification; Data Management; Data Integration; Decision Support; Online Analytical Programming (search for similar items in EconPapers)
JEL-codes: D81 G32 (search for similar items in EconPapers)
Date: 2021
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