Golden Copy: Compatibility and Collaboration Become Ever-Present

Working with data, whether for compliance, following standards or performing analysis, relies on a common approach

Looking at the feature stories brought to you by Inside Reference Data this month, it's astounding how some of the same operations functions and concerns touch so many different compliance, standards and data analysis initiatives.

Efforts to make standards compatible, as reported in "Managing Multiplicity," must begin with identifying business practices that are different, as Scotiabank's Sydney Hassal tells us. Firms must also, of course, contend with jurisdictional differences in standards, notes Allie Harris of Bank of Montreal. A key purpose of reconciling all these differences is to be able to manage risk data and, in turn, competently manage risk itself, as Thomson Reuters' Tim Lind points out in this story.

After this report laying out some of the challenges for achieving compatibility of standards, our "Interview With" article offers examples of how to achieve necessary collaboration. Cornelius Crowley of the Office of Financial Research, a US government entity concerned with financial industry standards, urges companies to drill down to "microdata"—granular, targeted and specific information on entities, instruments, transactions or products. Breaking down standards efforts to the level of individual data elements can increase accuracy, Crowley advises.

The same principles of collaboration or sharing, and adhering to a standard, albeit in the form of a data governance plan, can apply to support for data analytics, as reported in "The Right Frame for Analyzing Data." Both data governance and data analytics can be improved upon by building mechanisms to share data, in the view of Leif Hanlen, a business development executive at Data61, a data requirements organization that is part of CSIRO, a digital development agency backed by the federal and state governments of Australia. Yet, however advanced a data sharing system is, the rules that data vendors and exchanges have about their data make it challenging to join such disparate sets together, observes John Denheen of Tyler Capital in London. "One of the big issues with getting good reference data is that you need to be able to tack it on to other data sets," he says.

One service provider, the enterprise data management (EDM) company Xenomorph, is conscious of the proprietary nature of data and its impact on sharing and collaboration, as its CEO, Brian Sentance, relates in this month's Industry Warehouse. A "secure, auditable EDM system" can mitigate the risks of bad data from running analytics without consistent standards and with different complexities from different sources of data, says Sentance.

EDM, if defined and used correctly, could be another valid frame for analytics, just like data governance plans. The common challenges of compatibility and standards found in the different endeavors covered in these stories are increasingly being recognized, although no one is claiming to have definitive solutions, just some insights and advice on how to cope.

Looking further afield, at a model approach to regulatory compliance technology being taken in Singapore called the "regulatory sandbox," which is covered in "Speeding Fintech's Evolution in Asia," you may agree that allowing attempts at new data operations solutions without requiring providers to gamble on a boom-or-bust outcome could be a more reasonable philosophy and framework for collaborative data efforts that make use of compatible standards.

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