If companies don't have good data quality, their big data investments may not generate the insights they expect. In fact, delinquent cleansing efforts can put companies at risk, if their data management teams are responsible for processing security information among other types of data, such as customer information, revenue and social media posts, according to Waters Technology. Security information must be carefully processed and secured, or companies that handle it could run into compliance issues.
While most enterprises now understand the importance of data, they don't always place the same value on their data quality, according to Jim Harris, in a post for the Obsessive-Compulsive Data Quality blog. Just 34 percent of surveyed participants said their data quality could be described as "a trusted source of high quality enterprise data," or a 'data warehouse.' On the other hand, 65 percent said they had "untrusted sources of poor quality enterprise data" that were more like 'data outhouses.'
"Many organizations are still trying to become compliant by chasing their own tails without addressing the fundamental issue of everything they do, which is bad quality data," says Vangelis Tsianaxis, managing consultant for Deloitte's enterprise risk services unit. "There is no compliance data, risk data or marketing data. There are data assets that need to be managed in order to enable running the business and its different functions."
Rather than taking a reactive approach, firms need to invest in data quality tools so professionals can harvest insights from the vast amount of content being generated every day.
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