Investing in data quality can provide a range of substantial cost savings by improving the productivity of the analytical risk management process. A direct correlation exists between data quality and productivity improvements within the risk management function. Poor data quality can result in:
Increased time needed to develop models
Lower confidence in the model results
Less time to actually analyze results
Need for higher capital buffers and loss allowance provisions
There are several processes available for banks to define data quality, and guiding principles that can be implemented to improve data quality. Download When Good Data Happen to Good People to learn more on the guidelines for defining a data quality framework.
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