Opening Cross: If Data and Standards Were Easy…

Whether dealing with utility startups or commercial offerings, should you throw your weight behind making something a "standard," or back alternatives in the spirit of healthy competition?

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In a recent blog post, Richard Berner, director of the US Treasury Department’s Office of Financial Research (OFR), outlined how standards and high-quality “microdata” can help financial firms and regulators monitor risk and better fulfill reporting requirements, and how legacy systems, poorly integrated data platforms—for example, resulting from mergers of companies with different technologies—and prior regulatory environments that did not mandate standards to promote transparency, can result in opaque market data and increased risk.

This won’t be news to those who work with market and reference data on a daily basis, though OFR deputy director Cornelius “Con” Crowley recently presented a 13-page whitepaper to the European Central Bank, describing the OFR’s experience with standards, including the Legal Entity Identifier.

The problem with standards is that they need wide acceptance and adoption in order to become a true standard. And while standards may emerge from a landscape of multiple, competing codes or symbologies, continuing to try to compete against an emerging standard does little for the industry as a whole. 

For example, when members of FIX Trading Community, Pantor Engineering and SpryWare collaborated to develop the FAST Protocol for reducing the amount of bandwidth consumed by transmitting options data, CME Group shelved its own bandwidth-reducing process and threw its weight behind FAST. In fact, FAST quickly became a standard for data distribution, until a company asserted a patent infringement claim against it—eventually dismissed in court—and much of the industry dropped FAST like a hot potato. Who benefited from undermining this standard? No one—not even the company that brought the patent lawsuit. 

However some of the vendor-proprietary symbologies that their creators would like to see become standards are at least taking a more open approach, such as the Bloomberg-developed FIGI (Financial Instrument Global Identifier), which cost management platform vendor XPansion Financial Technology Services has now integrated into its XMon platform, allowing users to map to their preferred identifier.

Loosely speaking, standards aren’t limited to legal identifiers. For example, commercial offerings can also become an industry standard. Here, the issue is not so much about coalescing behind one “standard” but about ensuring interoperability and cooperation between competing ones. For example, CME Group and Thomson Reuters last week announced an integration between their messaging platforms, linking more than 300,000 industry professionals. But CME rival Intercontinental Exchange has its own messaging platform, as does industry-backed startup Symphony, in which many firms have a sunk cost that they would surely want to leverage.

And “intellectual” standards don’t only apply to data: they can—and arguably should—apply to many processes around data. For example, contracts, licenses, and terms of data use differ wildly by source and geography. And despite the efforts of industry associations like FISD and specialist vendors and consultants, data professionals must understand the legal ramifications of a contract, or be able to consult on-staff lawyers. This topic is discussed in this week’s Open Platform, authored by a team of law professionals who I’m sure would be only too happy to share their advice.

Because instead of being easy, “The more time we spend analyzing data costs and pricing, the more convoluted we find that it is,” says Amjad Zoghbi, director at XPansion FTS. People may complain about the cost of data, but part of that cost reflects the experience of those specialists who manage to make data management look easy, despite the fact that it isn’t. And while the industry takes leaps forward towards greater simplification and transparency on a daily basis, the evolution of financial instruments and new datasets often results in greater complexity. But if data was easy, it wouldn’t be so exciting.

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