Hospitals and pharmaceutical companies are nowadays working on drugs to treat big illnesses facing the human population and huge amount of money is being spent on clinical trials. Say, a trial suggests that a particular drug will work on 99.99% of the population and the rest 0.01% would die. Even after continuous trials, the companies can’t be sure whether the drug would work on a specific person or not. Just because of this reason, the drug is never released and remains locked in the R&D stores. Because of genetic sequencing now, the companies can actually figure out that a person ‘X’ would be saved by this drug and ‘Y’ would not. Five to ten years down the line, a time would come where doctors won’t be allowed to prescribe drugs to patients without a genetic sequencing test. The test would actually figure out what would work for one patient and what for another. This would require capturing, recording and analysing the records of genetic sequences of millions of patients which is nothing but ‘Big Data’ at work.
Big Data & Crowd sourcing
Another opportunity where ‘Big Data’ analytics have a huge scope is ‘Crowd Sourcing’. Consider a group of television viewers discussing quality of service of a DTH service provider on social media. There would likely never be a 100% consensus over the same. Viewers’ experiences would be
different, and there would be biases based on channel preferences, cultures & tastes of family and relatives and other factors. The challenge would be to create relevant information and knowledge out of this collection of data to provide information such as provider ratings and improvement guidance
Big data,
Big challenges &
Big judgement
However, the hype over big data is not without its criticism. ‘Big Data’ needs to be complemented with ‘Big Judgement’. Only 30% of the employees in most of the enterprises have the necessary skills and an understanding of the mature processes required to derive the relevant insights from the data. Also, ‘Big Data’ involves analytics over past and present data to predict about the future. However, if the inputs and factors affecting the system change its dynamics in the future, the past can say little about it. There are also concerns about the threat to privacy and personal information.
The enterprises today stand at an incredible time of opportunity, considerably better able to improve the world through computing than in any previous era. With that vast increase of power over information come weighty consequences. This is the moment when ‘Big Data’ steps in with a mission to narrow down the gap between ‘what is theoretically explicable’ and ‘what is practically achievable’.
“With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real. Big data isn’t about bits, it’s about talent”–Douglas Merrill