Numbers and data have become an indispensable part of our lives. Right from the common man to world leaders, everyone uses numbers and statistics to guide their decisions in areas of politics, entertainment, and even healthcare. We have complex algorithms, which are even available as apps, to help predict the spread of diseases. But as a recent research suggests, blindly following numbers and data without a context can be misleading, according to a press release Thursday.
“The Parable of Google Flu: Traps in Big Data Analysis” is published in the journal Science, funded, in part, by a grant from the National Science Foundation, where the author Ryan Kennedy, examines Google’s data-aggregating tool Google Flu Trend (GFT). GFT is a web service operated by Google and provides up-to-date estimates of influenza activity for more than 25 countries. It works by aggregating Google search queries on flu related topics and helps predict outbreaks of flu.
“Google Flu Trend is an amazing piece of engineering and a very useful tool, but it also illustrates where ‘big data’ analysis can go wrong,” said Kennedy who is a political science professor in University of Houston. Big data is a collection of large and complex data sets that allow capturing, searching, analyzing, visualizing, and sharing of data. Big data is used in areas of business, disease prevention, real time prediction of traffic, and crime fighting among others.