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Why analyzing Big Data can be bad for business

John Macdougall | AFP | Getty Images

Big data – where every aspect of your life is being quantified in every way imaginable – may be a term you are only just beginning to recognize. But get ready for another one: apophenia.

In the movie "Silver Linings Playbook," Robert DeNiro's character – a diehard Philadelphia Eagles fan – believes various random and unrelated factors such as the position of the TV remote controls and whether or not his son watches the game with him could factor into whether his team wins or loses.

While most people would refer to this as superstition, others might call it apophenia – the experience of seeing meaningful patterns or connections in random or meaningless data. The phenomenon arises from a subconscious tendency to seek out patterns – such as faces in clouds and hidden messages in music – because our brains are wired this way.

And, it can be bad for business, researchers say.

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"Big data tempts some researchers to believe that they can see everything at a 30,000-foot view," Danah Boyd, principal researcher at Microsoft Research and Kate Crawford, associate professor at the University of New South Wales wrote in a paper.

"It is the kind of data that encourages the practice of apophenia: seeing patterns where none actually exist, simply because massive quantities of data can offer connections that radiate in all directions," the paper noted.

Drawing inaccurate conclusions from big data analysis could prove costly for companies in how it influences decision making from advertising to management.

One example of big data analysis gone awry was Google, which developed Flu Trends in 2008 – a tool that geographically tracks searches for flu-related words over time. The idea was that people showing flu symptoms would search specific terms on Google to help self-diagnose and that these web searches could be used to create a real-time map of flu outbreaks.

While Google Flu Trends performed well for some time there was an anomaly in December 2012. According to an article in Nature magazine, Google's flu-case estimates were twice as high as those from the Center for Disease Control and Prevention. The cause? Researchers suggested that widespread media coverage of the U.S. flu season may have boosted flu-related searches, inflating the number of cases that Google's algorithm identified.

A pharmacy using this data to better decide on the appropriate inventory level of flu-related drugs could have easily overstocked on such drugs.

"Brands are becoming increasingly dependent upon data to manage their relationship with customers and to drive their businesses. Given this reliance, it's frankly pretty scary how data-driven decisions often seem to be arrived at and acted upon in a relatively unquestioning way," Colin Strong, managing director at GfK NOP business and technology told CNBC.

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"There will be very real commercial implications for companies that don't stop and question how these decisions are being arrived at," he added.

"Working with big data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth – particularly when considering messages from social media sites," Boyd and Crawford added in their paper.

Lack of candidates

While there is a growing awareness of the need to approach big data cautiously, a shortage of people with the skills required to provide accurate insights based on the data could lend to further apophenia.

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According to a 2011 report by McKinsey Global institute, by 2018 there will be 4 million big-data related positions in the U.S. that require quantitative and analytical skills. However, there will be a potential shortfall of 1.5 million data-savvy managers and analysts to fill these positions.

Linda Burtch, founder and managing director of Burtch Works, a U.S.-based executive recruitment agency for quantitative business professionals, told CNBC that there is still a shortfall in supply.

"The most common complaint among our clients is that there aren't enough candidates," she said.

According to a flash survey conducted by Burtch Works, in the first quarter of 2013, 89 percent of respondents said they were contacted via LinkedIn at least once a month with new job opportunities. Twenty-five percent said they were contacted weekly.

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The growth trend in demand for candidates with analytical and quantitative skills is expected to continue amid an explosion computing power and as developments in nanotechnology generate more data, she added.

"This is not a fad."

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