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Valuing data quality over massive quantity

Richard Jones Archive

With the amount of data floating in cyberspace rapidly growing, business leaders have become increasingly concerned about the prospect of analytics, anxious to figure out how they can use it to bolster their own operations. In this line of thinking, there's a competitive paranoia that inevitably sets in. CEOs and CMOs think - if I can't do more with my data, then what if everyone else in the industry does?

In the frenzied race to get more out of analytics, companies often act quickly and rashly - which means they run the risk of overlooking the importance of data quality. It's a simple and intuitive lesson, but it's one that many business leaders tend to forget - having data on your side isn't worth much unless that data is accurate.

Clean data helps companies tackle myriad challenges, ranging from better marketing campaigns to smarter sales pitches. With data, the focus shouldn't solely be on "more, more, more" - an equally effective rallying cry is "better, better, better."

The pressures of analytics
According to Insurance Tech, there's a great deal of pressure on business leaders today to get more out of data. That's because the amount of information being shared online is staggeringly massive - 98,000 new tweets appear around the world every minute, for example, and countless ones of them contain vital pieces of consumer insight.

Kelly Sheridan, associate editor at InformationWeek Financial Services, told Insurance Tech that the rapidly growing numbers are the impetus behind businesses' choices to ramp up their analytics efforts - but they had best be sure not to overlook quality.

"Plenty of businesses believe that big data is solely about massive data volume, but it is actually defined by its volume, velocity, variety, and validity," Sheridan noted. "Its volume is constantly expanding, its velocity and variety are increasing, but its validity continues to wane."

Data quality affects everyone
Companies can get themselves into trouble by shrugging away their data quality responsibilities, with individual employees telling themselves that it's not their problem. It's easy to let data issues fall to someone else - be it the tech team, the marketing department or the CIO. But the real truth is that issues of data quality affect everyone.

"Analytics is not a responsibility left to data scientists, as many organizations believe, nor is it entirely about software and tools," Sheridan argued. "The true meaning behind analytics involves enabling employees across the organization to formulate better questions and improve their decisions through valuable insight."

Clean data can help everyone, company-wide, achieve higher levels of success. But first, everyone has to do their part to contribute.