Working To Reconcile Data Management Standards

Many executives and even regulators will tell you that data operations in the financial services industry are bound to always have multiple standards, which they will use for identifiers, messaging and regulatory reporting, among others. Managing multiple standards, particularly for overall data management within a firm, and for compliance with specific regulations such as the BCBS 239 risk data aggregation principles, has become an unavoidable necessity.

This requires finding the common functions for meeting standards and complying with regulations, aligning different standards, and understanding which standards are becoming more prescriptive. First, to find those common functions, firms should identify which business practices are different, according to Sydney Hassal, associate director, global capital markets banking, Scotiabank.

"Different business practices and different domains create a lot of resistance with respect to trying to centralize and respond to regulations that are global in nature," she said, in remarks during June's Toronto Financial Information Summit.

Global firms are trying to figure out how to align data systems that operate for compliance with different jurisdictions' standards, so that data can be standardized to send to numerous jurisdictions at once, explained Allie Harris, director of data governance and analytics, Bank of Montreal.

"We work with the ISO [International Organization for Standardization] to try to bring jurisdictional standards into alignment, so we can use [a piece of data] once, and send it to six different regulators, because they will all expect the same thing," she said. "We are jurisdictional organizations playing a game at the global level. We have to understand our old ways of doing things jurisdictionally do not work. There's no other way than to standardize your data forms across the jurisdictions where you play."

Balancing Department Interests

Managing data standards for BCBS 239 data aggregation and reporting has become particularly challenging, according to Lida Preyma, director of global AML compliance at BMO Capital Markets. "There's no standard for compliance," she said. "Institutions don't know what standard they will be measured by for compliance with BCBS 239."

The challenge of managing standards applicable to risk data is compounded when addressing high-quality liquid assets and liquidity coverage ratios, according to Tim Lind, global head of financial regulatory solutions at Thomson Reuters. "These are slightly different between Basel [global standards], Europe and the US," he said. "It's hard to create one application to calculate those different numbers. We still see these at a regional level as opposed to risk data aggregation, BCBS 239, where you're not looking at data at the national level, but [throughout] the group or line of business."

Regulators are pursuing prescriptive standards for identification and semantics because the industry has not completed such standards, as Harris sees it. "We need to show our regulators that we can harmonize standards, that we are the experts in our business, in data management, and we can provide this as an industry," she said. "80 to 90% of data scientists' time is spent knitting data sets together. There is no great competitive advantage in creating a unique identifier inside your bank. Even if we take away 10% of the time we all spend knitting data sets together and put that time to something else, all our productivity goes up."

Data management can bring regulatory compliance departments and business interests together to support standardization and reconciling standards, according to Lind. "The heads of the business [including trading and asset management] are looking at the compliance person to tell them what business impact a rule will have. The compliance person thinks those who run the business should be able to understand [a rule] and figure out what it says, but it's written in legalese.

"We [in the data management profession] understand terms like ‘governance' and ‘stakeholders,'" Lind added. "We see data coming from an external source into a centralized facility, subjected to a set of rules and then propagated in a consistent way to downstream stakeholders. The data management operation has learned how to connect disparate lines of business, applications and geographies together in a dialogue about what's good for the institution. Translating information into semantics, classifications, codes and taxonomies is what data management people do."