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5 cases where big data was a big flop

Noah DMello | July 15, 2016
You thought big data is your answer to everything? Think twice. Sometimes your data can’t be trusted. Here are some examples of bad analytics that point to trust issues.

5 cases where big data was a big flop

Big data may be the technology that everyone's talking about, but that doesn't mean it is flawless. Big data has created havoc in some cases-and the reasons can be anything, such as detection of false positives, lack of tools, technical glitches, low quality data, wrong data, or unnecessary data.

With such errors, it may be possible that the results may be completely different from what you expected. Moreover, the results are sometimes not analyzed, which can lead to unpleasant results.

Let us take a look at some examples where big data went bonkers.

Google is feeling under the weather:

Probably the biggest and the most well-known big data failure was Google Flu Trends. This web service was started in 2008 with the aim of predicting flu outbreaks in about 25 countries. The logic was simple-just analyze Google search queries about flu in a particular region. This was compared to a historical baseline of flu activity level in that region, and based on the results, the activity level was reported as low, medium, high or extreme.

Sounds cool, doesn't it? But it wasn't.

At the peak of the 2013 flu season, GFT failed-and how. It was off by a whopping 140 percent! How did this happen? The algorithm was flawed and did not consider several other factors. For example, searching for terms like "cold" and "fever" did not necessarily mean that people were searching for flu-related diseases; they might just be looking at seasonal diseases. In 2009, it also missed out on predicting the outbreak of H1N1 entirely. GFT could not recover from this flu, which ultimately led to its untimely demise in 2013.

Targeting the wrong audience:

Big data is good and sometimes it can give you the right answers all the way. But sometimes, you have to pay the price for predicting everything right if you don't use the results wisely.

Well, Target learnt this the hard way. It ran a lot of algorithms and analysis on customer information, such as shopping trends, what they're buying, where are they buying from and personal information like anniversaries, birthdays, and marital status. With extensive data crunching, Target targeted (pun intended) expectant mothers through their buying patterns and offered them personalized pre- and post-natal items.

The idea seems wonderful from a marketing point of view, but one instance caught them off guard. A furious father stormed into Target for offering his teenage daughter these deals via mails. He was shocked as to why Target would give such recommendations to his innocent daughter. But what he didn't know was that his teenage daughter was indeed pregnant and was hiding it from her parents.


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