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Reference Data: Current Solutions Lacking, Despite Foundational Nature of Reference Data RDR

Reference data management (RDM) is a foundational element of financial enterprises, yet the collection of solutions used to manage reference data in most firms is not satisfactory, according to a report published this week.

The report – Reference Data Management: Unlocking Operational Efficiencies, published by Tabb Group in conjunction with data integration specialist Informatica – describes current sentiment around RDM. It looks at development through four generations of solutions, details the obstacles to RDM success and sets out how firms at different levels of RDM adoption can move forward towards the holy grail of centralised RMD coupled to consistent reference data processing.

Despite huge investments in RDM over the past decade, research carried out among 20 firms – 25% in Europe, 75% in the US, 50% on the buy side and 50% on the sell side – in April 2012 found 86% of respondents dissatisfied with their RDM capabilities. Of these, 48% are being driven to improvement for reasons related to resource optimisation and outcomes, while 35% are responding to specific catalysts such as compliance.

For details on the report click here.

Recommending how to navigate the road ahead, the study suggests firms committed to bolstering existing suites of RDM solutions should focus on wrapping current solutions with technology that enables a consistent enterprise data governance process, while those yet to make a significant commitment to an RDM solution should seek solutions that manage multiple reference data domains in a consistent and integrated enterprise framework.

The report concludes: “There can be no glory without doing the hard work first. Data fluency, a critical precursor to data consumability, simply means that data flows more easily, which in turn means that end users must be able to find it. And, finding data requires meticulous attention to standards, labels and other metadata, however imperfect they may be now or in the future. That way, no matter how big or complex the data gets, end users will have a much better shot at harvesting value from it.”

Source: Reference Data Review, 19.10.2012

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