OFR's Data Officer Talks About Finding Compatibility Among Standards

To comply with financial industry regulation, firms need access to data and the ability to collaborate internally on management of that data. They must also report that data correctly.

The keys to that collaboration can be found in standards, says Cornelius Crowley, deputy director and chief data officer at the Office of Financial Research (OFR), the unit of the US Treasury that promotes adoption of identification and communication standards in the financial industry. Standards exist for many different functions, including messaging, product identification and transaction identification, and as many as 80 other specific data elements, observes Crowley.

The Committee on Payments and Market Infrastructures (CPMI), part of the International Organization of Securities Commissions (IOSCO), has a data harmonization working group focused on those product and transaction identifiers. Also, the International Organization for Standardization (ISO) administers standards such as ISO 20022, the standard for financial industry messaging. One way complications occur is when "one standard incorporates another for reference," says Crowley.

"ISO 20022 has a data dictionary within it, so in informing messages, you can refer to other standards," he adds. "It can refer to requiring the use of the LEI [legal entity identifier] as a counterparty identifier. ... Standards cover the gamut, from full message description to individual data elements such as the LEI. It's not one or the other; it's often how you use them together."

The CPMI/IOSCO data harmonization working group has increased its focus on standardizing derivatives data, in the form of unique trade identifiers (UTIs) and unique product identifiers (UPIs). This relates to the issues US regulators had with a lack of universal standards for swap data repositories. Crowley and the OFR are working with colleagues in the Financial Stability Oversight Council (FSOC), which is also part of the US Treasury, and CPMI/IOSCO, to achieve more standardization of derivatives and swaps data.

The Microdata Dawn

Overall, a new term relevant to data standardization is emerging—"microdata"—which is financial data that describes entities, instruments, transactions or products. Microdata is granular, meaning it is more targeted and specific, and it is an important element for standardization of data—microdata should be aggregated to manage risk. Crowley adds that companies must improve back-office data operations to more easily produce, link and integrate information.

The use of standards doesn't automatically improve data quality, but it does help ensure "everyone is speaking the same language," says Crowley. "Having a common basis for understanding the definition, meaning and syntax of a data element—for example, with coupon interest, how many decimal places it contains—makes the exchange of data that much clearer, and leads to improved data quality."

Setting a common standard for any data element prevents having too much latitude when populating the element, and therefore ending up with bad data, says Crowley. "For example, if you ask for a date but don't specify the use of the existing time and date standard, or that you want it in month-day-year order rather than year-month-day order, or that it should have slashes instead of dashes—these are small things, but they make a difference in being able to collect the data with confidence in its quality," he says. "With a common standard, you're referencing the same metadata format and increasing the opportunities to get the data right."

Standardization promotes accessibility, and access is necessary for even the highest quality data to have value, says Crowley. Accessibility also encompasses sharing of information between regulators and firms.

"Where regulators are collecting data and making the metadata and templates about these collections available to other regulators and the public, this improves the ability to share the data," says Crowley. "Firms will have better insight into what data the [regulatory] agency is collecting, how they describe it, what data contents they are expecting and how the data elements are formatted."