Major Retail Bank
In order to quantify asset risk and comply with regulatory reporting requirements such as the Dodd-Frank Act, this leading retail bank is using Datameer to validate data accuracy and quality.
Integrating loan and branch data as well as wealth management data, the banks data quality initiative is responsible for ensuring that every record is accurate. The process includes subjecting the data to over 50 data sanity and quality checks. The results of those checks are trended over time to ensure that the tolerances for data corruption and data domains aren’t changing adversely and that the risk profiles being reported to investors and regulatory agencies are prudent and in compliance with regulatory requirements.
Prior to Datameer, the bank was using Teradata and Netezza and building out datamarts in order to analyze data quality using their SAS application. The process was time consuming and complex and the datamart approach didn’t provide the data completeness required for determining overall data quality.
The data quality/data stewardship team of 15 users are utilizing a 20 node Cloudera Apache Hadoop cluster. They are analyzing trillions of records which currently result in approximately 1 terabyte/month of reports. The results are reported through a data quality dashboard to the Chief Risk Officer and Chief Financial Officer who are ultimately responsible for ensuring the accuracy of regulatory compliance reporting as well as earnings forecasts to investors.
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