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Implementing an Enterprise Risk Data Warehouse

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Building an RDW presents all these challenges and more, since there are enough variations to make this a treacherous and difficult task in the best of times. Some of these challenges are:

  • Source Systems Changes: Regulatory requirements are forcing changes in source systems. Because of compressed timelines, it is entirely possible for source systems to be changing in parallel with data warehouse development.
  • Heightened Need for Accuracy: RDWs, used for sensitive reporting, have far higher quality standards than their marketing counterparts (i.e., CRM, etc.). There is consequently a need for a process to modify incorrect data within the warehouse in advance of source changes.
  • Dealing with Organizational Silos: An enterprise RDW also needs to collect data from disparate parts of the organization that are not adept at working together. Incompatible reference data, overlapping data sets and irreconcilable terminology are some typical data warehouse problems, which are exasperated by the enterprise-wide nature of the task.
  • The Funding Challenge: While data warehouses used for business improvement can be supported by ROI computations, funding an RDW is much more difficult. Risk management functions at its best when all risks are avoided. The lack of materialized risks in turn makes it difficult to fully appreciate the value of risk management investments; at best, one can anecdotally point to cases where a lack of risk management has led to huge losses. To be sure, several organizations are attempting to use metrics to tie risk events to resultant costs, but there is typically a qualitative component to these measurements, which leave them open to debate.

Life Cycle of a Enterprise Risk Management Implementation

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