Despite the merits of efficient data management in reducing operational and other risks, financial firms will only modestly increase their expenditures on data management projects to $1.43 billion in 2014 from an estimated $1.2 billion this year, according to a report issued on Monday by research firm Aite Group in Boston.

Driving the increase in spending on data management projects, Aite Group said, will be the need to improve exception-based workflows; reduce the costs of acquiring data from external vendors; support new regulatory or compliance systems; improve communications with clients and counterparties; and improve liquidity management.

However, as Aite Group noted, the data management projects now underway must meet specific business rationales as winning support from upper management for more comprehensive initiatives remains difficult.

“Data management professionals never reached real consensus on the technical approach necessary to create centralization of data but held onto it as the way to achieve a high-quality data management practice,” said Aite Group in the report entitled “Data Management Update 2010.” “This notion is no longer completely accepted.”

That means that data management projects have gotten substantially smaller in scope and are more devoted to a particular line of business. Such an approach has given rise to both the centralized and federated approaches to data management.

“The challenge will be for professionals and vendors to step in and provide the business rationale and tools to extend these narrower projects, creating a more cohesive data management whole across the enterprise,” said Aite Group. “First and foremost, these projects must be completed on time and within the promised functionality; whether internally built or sourced from a third party, the best validation a project can get is a client endorsement.”

Aite Group recommended that firms mix quantitative and qualitative factors into presenting a case for a data management project. That means offering immediate cost and operational efficiencies in the context of greater data quality, timelines and control.


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