A significant research has put 'big data' collections from platforms such as Google, Twitter and Facebook in the dock, stating that campaigns and companies can manipulate these platforms to ensure their products or issues
To cement their view, authors examined Google's data-aggregating tool Google Flu Trend (GFT) which was designed to provide real-time monitoring of flu cases around the world based on Google searches that matched terms for flu-related activity.
"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 Ryan Kennedy, a political science professor at University of Houston.
He and co-researchers detailed new research about the problematic use of big data from aggregators such as Google.
The GFT tool that set out to improve response to flu outbreaks has overestimated peak flu cases in the US over the past two years.
It overestimated the prevalence of flu in the 2012-13 season as well as the actual levels of flu in 2011-12 by more than 50%, according to the research.
Additionally, from August 2011 to September 2013, GFT over-predicted the prevalence of flu in 100 out of 108 weeks.
"Many sources of 'big data' come from private companies, who, just like Google, are constantly changing their service in accordance with their business model," Kennedy said.
We need a better understanding of how this affects the data they produce; otherwise we run the risk of drawing incorrect conclusions and adopting improper policies, he added.
The team also questions data collections from platforms such as Twitter and Facebook like polling trends and market popularity.
The researchers contended there is room for data from the Internet to combine with more traditional methodologies to create a deeper and more accurate understanding of human behaviour.
"Our analysis of Google Flu demonstrates that the best results come from combining information and techniques from both sources," Kennedy noted.
Instead of talking about a ' big data revolution', we should be discussing an 'all data revolution' where new technologies and techniques allow us to do more and better analysis of all kinds, said the study published in the journal Science.