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Assuming data quality can be risky for many businesses

There's a growing sentiment in the minds of some business leaders that ensuring data quality should be a top priority. Before companies can take action based on their databases of knowledge about their clientele, they must first purify it by using address management tools and other solutions that will root out errors and distil accurate, up-to-date information.

This seems like a given. Some marketing executives, however, disagree. Rather than spend time and money working toward murky standards of quality, certain companies choose instead to act on "the assumption of quality," hoping that the information they've gathered is already accurate.

This strategy helps companies save time and embark on their big data initiatives without skipping a beat, but it's also risky. Making false assumptions about quality can lead to marketing blunders or ineffective customer service initiatives, not to mention potential fraud.

Jim Harris, author of the OCDQ Blog, recently lamented that despite the risks involved, some companies are leaping to conclusions about the purity of their data anyway. The practice may be perilous, but some business leaders have the willingness to live dangerously.

"While this situation has always existed, the Internet and the era of big data is exacerbating it," Harris wrote. "I would argue that many, for lack of a better term, traditional data and information management applications, have functioned off of the same assumption of quality even though data and information quality best practices are implemented."

As competition among corporations heats up, there's a growing sense of urgency to act as quickly as possible on the insights that can be gleaned from big data. Companies assume that by skipping the step of ensuring quality, they can speed to the finish line and gain a competitive advantage on their rivals. This plan can easily backfire, however, and wise executives know that quality should always come first.

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