There is a lot of fanfare around big data. But there’s also a surplus of misinformation floating around about the potential for leveraging the data deluge from mobile, social networks and other unstructured data sources.
Author Phil Simon has assembled the four biggest myths of big data. We’ve added a fifth from a recent Businessweek article noting that slow and steady doesn’t win the race when it comes to effectively leveraging big data.
Simon’s myth roundup is as follows:
1. An organization can store and retrieve all the data on one subject. “Never before has so much data been available to us. Forget megabytes and petabytes, exabytes of data now exist,” he notes. “When you search, you are accessing anywhere from 4% to 6% of all information on the Internet. You will never get all of the data.”
2. Business requires all of the data to make an informed decision. Simon notes that companies that are effectively leveraging the power of big data realize they will never capture all of the relevant information and they can mine through new data sources to identify the truly valuable ones.
3. With big data comes certainty. While big data can reduce the uncertainty of a merger, product launch or new venture, it is virtually impossible to eliminate all uncertainty. “Analyzing petabytes of unstructured data may well help companies better understand customer sentiment. However, don’t make the mistake of assuming that big data eliminates all variability,” according to Simon. “The fluctuations of life and business will still throw a wrench into the best laid plans.”
4. Big data is a fad. While the terms big data and data science may be replaced by other buzzwords, the notion of using data analysis to adeptly sift through volumes of data for actionable insight to bring value to the business will not go away, Simon predicts. “Foolish is the professional, however, who believes that data is a fad. It’s high time that organizations recognize the importance of big data,” he adds. “Refuse, and your company may not be around when the light bulb finally goes off.”
And, finally, too many companies forget to account for the velocity of big data, notes Nick Millman, who leads Accenture Digital, Data & Analytics in Europe, Africa and Latin America.
5. Focusing only on the volume and variety of data is enough. Instead, he advises companies to focus on the pace at which data can be collected and analyzed to generate insight executives can act upon. “Imagine you’re an insurer trying to fight fraud,” he says. “Finding out three months after paying out on a fraudulent claim is clearly less valuable than identifying the fraud while processing the claim.”
He advises companies to consider new technologies, such as advances in computer memory and databases to speed up the “time to insight” from data analysis. In addition, he suggests companies hire statisticians who can build speedy models to analyze data.
“Increasing data velocity isn’t just an obscure objective; it’s a business necessity that gives companies a chance to open up a lead on competitors,” he says. “After all, what good is big data if you move like a lumbering giant?”
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