In order to quantify asset risk and comply with regulatory reporting requirements such as the Dodd-Frank Act, this leading retail bank is using Cloudera and Datameer to validate data accuracy and quality.

Integrating loan and branch data as well as wealth management data, a major retail bank’s 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. They also ensure that the risk profiles being reported to investors and regulatory agencies are prudent and in compliance with regulatory requirements. 

Prior to deploying Cloudera with Datameer, the bank was using Teradata and IBM Netezza to build out data marts so they could analyze data quality using their SAS application. The process was time consuming and complex, and the data mart approach didn’t provide the data completeness required for determining overall data quality.