The Risk Rationale

michael-shashoua-waters

Last week in this space, I observed that efforts to centralize reference data are increasing and suggested that the industry include proposals to obtain data from its original source, the issuer, in any centralization and integration of data.

Something else that also ought to figure into conversations about data management methods is the role of standards in organizing data for such a centralization effort, as well as for identifying how data was generated. These are the necessary tools to implement centralization, with or without more direct, original sourcing for data.

The EDM Council and Object Management Group (OMG) have been working for months on their Financial Industry Business Ontology (FIBO), a semantics standards initiative, and in recent weeks, the OMG's internal finance and architecture departments signed off on the specifications of FIBO.

Alongside past and present standards initiatives such as XBRL and the legal entity identifier, what all these data management efforts are aimed at is reducing risk. The industry appears to be getting increasing cooperation, both among major global firms and among different markets and countries worldwide, when it comes to such standards.

The increasing collaboration, when it comes to FIBO at least, is fueled by "mutual self-interest," as David Saul, a senior vice president and chief scientist at State Street Corporation who works on FIBO with the EDM Council and the OMG, puts it. "Reducing risk is something we all have to do," he says. "Semantic technology and an ontology gives us a standard way to communicate when talking about entities and relationships." The result is that data management professionals can write programs based on FIBO indicating equivalent items and therefore reducing data translation needs.

Part of my conclusion in last week's column was that increasing data accuracy would result from sourcing data closer to its origin. Having data semantics standards in place, using FIBO provides another piece of the puzzle that data managers have been trying to put together, whether they centralize all their data or not.

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