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"Most enterprises don't fathom the magnitude of the impact that data quality problems can have," said Ted Friedman, principal analyst for Gartner. "These problems cause wasted labor and lost productivity that directly affect profitability."
Moreover, the majority of large enterprises continue to reach for ineffective technology solutions even after they identify data quality problems, he said. These ineffective solutions often include priority spending programs for advanced business intelligence and customer relationship management (CRM) capabilities.
These programs fail, in large part, because the poor quality of underlying data is not recognized or addressed.https://o1.qnsr.com/log/p.gif?;n=203;c=204650394;s=9477;x=7936;f=201801171506010;u=j;z=TIMESTAMP;a=20392931;e=i To solve the problem, many enterprises first look to off-the-shelf technology without first focusing on people and business processes.
"Throwing technology at data quality issues usually doesn't solve the problem and won't yield positive long-term results," he said.
Instead enterprises should examine organizational approaches and methodologies to improve data quality, said Friedman. Companies must also actively engage the people who use the technology to achieve business objectives, not just technology staff.
"If the IT group is the only organization that actively works and focuses on the issue, the business's ability to achieve data quality goals will be severely limited," Friedman said. "The greatest success in managing data quality comes from engaging both business users and the IT organization."
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This article was compiled and edited by CIO Update staff. Please direct any
questions regarding its content to Allen Bernard, Managing Editor.
This article was compiled and edited by CIO Update staff. Please direct any questions regarding its content to Allen Bernard, Managing Editor.