Big Data, Good or Bad?

Posted on by Mincheol Shin

Big data often refers to datasets that are so large and complex to be managed by traditional data processing methods. Although Big data turns out to be a thing that would critically exert an influence over people’s life, there exists a controversy on whether it could always result in positive outcomes. Numerous data experts note that the strength of Big data is dominantly in its predictive power whereas the weakness involves the problems such as the bad or biased quality of initial data that often generates inaccurate decisions, and privacy issues.

Throughout the history of inferential statistics, the size of data has long been an issue in terms of the generalizability of results, which relates the concept of statistical power, and statistical methods such as the bootstrapping and Monte Carlo simulation have been suggested as an alternative to overcome the limitation of small sample sizes. Accordingly, Big data was deemed helpful and attractive to many researchers (and practitioners) since it assures “big enough” sample size at the initial stage of analysis; however, Big data engages some negative aspects which could mislead the predictions and decisions if it is not carefully utilized.

In this sense, Big data should not be considered as a mighty tool and should always be approached with caution. Unless the quality of data and policies that prevent privacy violations are not warranted, Big data would rather bias people’s decisions and end up abusing a human right.

 

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