Reference data managers may see centralized data as a road to efficiency, but shouldn't forget about accuracy and risk management in the process
A vital element of dealing with reference data is in danger of getting lost amid the most recent round of attention being paid to the legal entity identifier (LEI). That element is efficiency, an executive of a data integration provider told me recently, relating remarks made to him by a prominent Wall Street chief data officer.
Efficiency can mean a lot of things, however. Connecting separate silos of data can be part of that, spurred by new regulations and new standards agencies. In a chief data officer's mind, efficiency means better leveraging data and technology, and getting support from service providers to do so. Firms shouldn't have multiple data quality programs that all perform the same function.
Centralization of data can be a road to that efficiency. In January, Inside Reference Data reported how cloud computing advances have enhanced the ability to centralize data for analytics. To centralize data management, efficiency is inherently required because, as that data integration firm's executive points out, automation, exception handling and responses to errors are all now more important to centralization than just staffing and processes. These functions are all steps to achieve efficiency.
Getting firms to see data management as something other than just a support function is necessary, because data management experts are needed to understand and analyze centralized data. That requires spending on something other than revenue-producing functions for a firm—and being able to mitigate or prevent the risk from a corporate action failing at a cost of millions of dollars really should be considered a revenue stream.
Although some staffing expertise is needed to support centralized data, and the inherent efficiency that can produce, the data management functions that also fuel that efficiency shouldn't blind data managers to the value that can come from accurate, risk-reducing data operations.
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