Survey Says: Poor Data Quality Is Costing You Money
Over 90 percent of respondents concede they haven't invested in modeling and analysis tools to help them make sense of inaccurate, incomplete, or obsolete data.
Think you have your data quality issues licked? You’d best think again, says a new survey conducted by consultancy Risk Waters Group and SAS Institute Inc., which finds that inaccurate, incomplete, or obsolete data are causing financial institutions up to $120 million yearly.
Risk Waters and SAS surveyed 400 risk managers at 300 financial institutions as part of an effort to learn more about the losses that financial institutions incur through operational risks—and what they’re doing to reduce these losses.
Not surprisingly, data quality issues topped the list, with 28 percent of survey respondents reporting that difficulty associated with collecting the volume of data required to accurately identify and manage operational risk represents the major obstacle to preventing losses. Another 33 percent explicitly blamed poor data quality as a major stumbling block that contributes to these losses.
The good news is that organizations are responding to these issues in a variety of different ways. Many (50 percent) say that they have implemented internal loss databases, while others (45 percent) have created self-assessment tools.
The bad news, however, is that an overwhelming majority—90 percent—of respondents concede that they haven’t yet invested in modeling and analysis tools to help them make sense of this data once it has been collated.
"Data has little value unless it can be turned into worthwhile information and, as yet, many companies seem unwilling or unable to take this step,” says Peyman Mestchian, head of risk with SAS UK.
What’s holding them back? Most respondents cited the high cost of these tools, along with a perceived lack of functionality in existing software packages, as their primary hang-ups. As many as 68 percent of those surveyed have built their own operational risk systems, however, both because they can justify the cost and customize the solution to meet the specific needs of the business.
SAS’ Mestchian acknowledges that in-house systems are better than nothing, but cautions that they often lack features and flexibility. "In-house operational risk systems have done a reasonable job up until now, but only a minority of those software systems are both scalable and flexible enough to cope with what is now being asked of them. Trying to update outmoded systems is proving costly, time-consuming and often ineffective," he writes.
Stephen Swoyer is a Nashville, TN-based freelance journalist who writes about technology.