Managing Risk special report

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Technique or Technology?

The importance of certain data management functions for competent risk management is increasingly evident in the securities industry. Data modeling, data governance and data quality monitoring are among the tasks of concern, as Michael McMorrow of Allied Irish Bank relates in the this issue's Virtual Roundtable.

The biggest challenge, however, to hear professionals tell it in the Roundtable, and in this report's Q&A with Tower Group's Rodney Nelsestuen, is getting that quality data, and making sure data is complete. To obtain that completeness, firms must tie issuer-level risk attributes to specific issued securities, says Ludwig D'Angelo of JPMorgan Chase. But can data quality be had by double or triple sourcing reference data? D'Angelo believes that's not worth the risk of bad data this presents because of varied standards for data quality. He suggests rating vendors as one way to proactively manage data quality.

Securities industry data has a lot of attributes or fields, and increased granularity, observes Sybase's Sinan Baskan in the Roundtable. As a result, providers and consumers have to address volume, workload and timeliness issues in data management, to make critical risk decisions, he explains. To accomplish this, the industry has less and less time, Baskan adds, and the penalties for bad data in risk accounting are becoming steeper-both compliance-wise and business-wise.

So what can a data manager do? Aside from improving tools and operations, the risk management unit of a firm should include a data management team, says Baskan. Nelsestuen says that firms with the best practices are the ones with dedicated data teams. Getting quality data requires a quality data management staff. Technology and tools may only go so far.

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