Big Data does not necessarily mean Good Data. And that, as an increasing number of experts are saying more insistently, means Big Data does not automatically yield good analytics.
If the data is incomplete, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals.
One of the classic stories of how data out of context can lead to distorted conclusions comes from Harvard University professor Gary King, director of the Institute for Quantitative Social Science. A Big Data project was attempting to use Twitter feeds and other social media posts to predict the U.S. unemployment rate, by monitoring key words like “jobs,” “unemployment,” and “classifieds.”
Using an analytics technique called sentiment analysis, the group collected tweets and other social media posts that included these words to see if there were correlations between an increase or decrease in them and the monthly unemployment rate.