In 2008, Google took another step towards omniscience. The company claimed it could forecast outbreaks of flu, weeks before the US Centers for Disease Control and Prevention, by intelligently using search data. For a while, it succeeded. Google had authored yet another big data success story. But, earlier this year, researchers from Northeastern and Harvard universities uncovered a rather large problem with Google Flu Trends: it was no longer accurately predicting flu cases – overestimating the number of cases in the US for 100 of the previous 108 weeks, by up to twice as many.
And the reason for this inaccurate reporting? Google’s own search algorithm and its auto-suggest feature. Type ‘Do I have f’ into Google and it suggests ‘Do I have flu’, which may have led to people searching for flu when in fact they were interested in knowing if they had food poisoning or even fibromyalgia (a chronic condition that causes pain all over the body).
Google’s focus on correlation rather than causation (scientists are clear that “correlation does not imply causation”) led to what the researchers termed an embarrassing case of “big data hubris”. In other words, it forgot some of the core principles of statistics and mixed big and small data in a way that proved problematic.
Can business and, in particular, HR learn anything from the parable of Google Flu Trends? Of course, most HR departments are nowhere near as sophisticated in their use of data, but there’s no ignoring the steady march of HR analytics. The question is: is anyone actually getting it right? Or is the profession in its own small way following Google’s precarious path, seduced by the possibilities of oh-so-sexy big data and attempting to measure too much, collecting data on meaningless things or starting its explorations from the wrong place
This article has been extracted from http://www.hrmagazine.co.uk, please click on this link to read the article in full http://www.hrmagazine.co.uk/hr/features/1144056/hr-add-value-analytics
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