Big Data – More Headache Than Elixir


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In the past two years, I have ended the year writing about different charities. In 2011, I wrote about charity: water, and in 2012, I talked about Room to Read. This year, I want to do something different. I’m going to share a few brief observations I’ve made about one topic that came up in 2013.

Big Data

One thing I heard often throughout 2013 was “big data,” which was often heralded as a viable solution to what seems like everything. However, I rarely hear people talking about how fruitless having data is if there is no way to decipher and translate that massive amount of information into coherent, intelligible, and useful actions.

Having big data is equivalent to conducting a literature review for a PhD dissertation. There is (usually) so much information out there that a doctoral student must sift through and make sense of it all. It is painstakingly laborious, intensive, slow and very easy to be led astray and chase rabbit trails because there is so much data and everything seems interesting, although not necessarily relevant, to your own topic.

To me, big data is not a panacea. It never was. Big data is information on a large scale. Nothing more. If you collect massive amounts of information but do not know what to do with it or how to use it, then it is useless. One other observation is that data is tricky and can be “interpreted” in different manners depending on the method(s) used and the viewpoint of the individual(s) doing the interpretation. This may come as a shock to some and not others, but if a researcher is not careful, s/he will let bias creep in and arrive at the results that s/he originally sought, even if the results really did not reveal this.

The lesson is this: You can arrive at all sorts of conclusions from big data, but be careful. While some or many of these conclusions may make sense numerically, they may not make any sense contextually. In other words, just because you arrived at some numerical values from your analysis of big data, it may, in fact, not be pertinent to your original query.

Written By: Steve Nguyen, Ph.D.