Datactics has implemented its RegMetrics data quality reporting solution at a Tier 1 global bank. The bank is using the software to monitor over 10 million underlying records, which requires hundreds of millions of data points to be processed. The bank is also building predictive analytics on top of the RegMetrics framework to monitor data quality problems and make predictive assessments of data areas that should be prioritised for improvement.

The US bank’s chief data officer (CDO) assessed several data quality products before selecting Datactics, running a proof of concept and working with the vendor to develop a CDO dashboard within eight weeks. The system went live in January. The bank was previously sampling data and using some automation to check data quality, but was not able to monitor its entire universe of data in a timely way.

The deployment of RegMetrics is based on the bank’s data warehouse and uses five dimensions of the Enterprise Data Management Council’s Data Management Capability Model (DCAM) – completeness, conformity, accuracy, duplication and consistency – to ensure data quality and allow bank staff to monitor data using these metrics. As well as reporting on data quality and ensuring data delivered to business applications is accurate and fit for regulatory reporting in line with data quality demands of regulations such as BCBS 239, RegMetrics will be used to scrub and aggregate information for automatic submission to the SEC and US Federal Reserve.

Luca Rovesti, lead data consultant at Datactics, explains: “RegMetrics automates corrections when there is a clear breach of the test conditions assigned to the data, for instance when maturity dates are in the wrong format or when counterparty data is found in the wrong location. A small portion of failing data requires human intervention. In these instances, RegMetrics alerts business owners about failing records that need to be investigated and fixed via our ServiceNow solution.”

Stuart Harvey, CEO at Datactics, says RegMetrics allows the bank to maintain higher service level agreements (SLAs) on measures of quality with its data providers, bringing down the cost of fixing data that is paid for. Over time, the solution will also significantly improve the bank’s data, avoiding the need to use intermediate versions.

For Datactics, the bank is its third major customer in banking, adding to a total of over 10 customers in the financial services sector and a total of over 50 customers across a variety of industry segments.