Data is integral to everything financial organizations do. Beginning with a business deal, transaction or trade, data is aggregated, manipulated, named, channeled into systems, renamed, processed, parsed, and analyzed by people and applications relying on its accuracy to determine the next decision.
But while data management was once considered strictly an IT issue, the credit crisis has changed the way data is viewed by financial organizations. Today it is recognized as the business driver that it is, one that is central to the enterprise and key to its survival.
"It's no longer data management for its own sake, but it's to serve business drivers," says Fritz McCormick, a senior analyst with Aite Group.
Reflecting the cultural shift toward data as a business driver, organizations are hiring chief data officers and proactively reexamining their data management policies and procedures from end to end, experts say. "Businesses need to focus on cost and compliance at an individual and at a corporate level," observes Ron Ruckh, managing partner at consulting firm LEGG Smart. "One component that flows in these directions is data -- on the business side, not just on the IT side."
Indeed, the responsibility for ensuring data quality has been transformed from an IT task to a pervasive business priority. That transformation largely has been driven by risk management and compliance demands. >>
"All that analysis about regulatory reform and systemic risk has led the world to understand the importance of data precision, complex processes and the ability to trust your models," says Michael Atkin, managing director of the Enterprise Data Management (EDM) Council, a nonprofit trade association. "That has been a kick forward for the practice of data management."
Financial organizations are driven by profitability, fear of doing something wrong or of damaging their reputation, and regulation, Atkin suggests. And post-crisis, regulators are pushing hard for more transparency at financial organizations. It all begins with accurate data, he says. "It might be hard to make the case [for data management] on profitability or reputational issues [alone], but regulation has emerged as the current driver," Atkin contends.
"Failed trades, errors on client statements, erroneous performance calculations and inaccurate risk measurements, along with a host of other costly, embarrassing and potentially disastrous outcomes, result from poor data quality," wrote Fred Cohen, group VP and global head of outsourcing in consulting firm Patni's capital markets and investment banking practice, in a recent report on reference data management. In the current regulatory climate, he tells WS&T, when clients are evaluating their reference data management, they are first preoccupied with reducing risk rather than cost. "Previously, cost was No. 1. Now it's No. 3, after risk and efficiency," Cohen says.
The risk of poor data extends beyond operations to investor relations. With regulators publishing new rules on the accuracy of data on advertising materials, financial firms have been forced to pay much closer attention to both the content they do put out to the market as well as the information they don't put out, according to Conor Smyth, SVP of global sales for data management vendor MoneyMate, which last fall signed a deal with Schroders to provide the investment manager with a managed service to control the aggregation and cleansing of product information for presentation on its website. "Asset managers need to mitigate the risk of communicating incorrect or out-of-date information to the market," Smyth comments.
Gerard Walsh, head of web and CRM at Schroders, commented at the time of the deal that the firm's decision to hire MoneyMate stemmed from an understanding of the significance of the data management challenge the firm sought to address. "The focus of this initiative is to present accurate, up-to-date and error-free product information on our websites," he noted.