According to an Accenture study, when preparing to make strategic decisions only 21% of UK and US company professionals rely on complex data analysis while a great majority still considers a mix of high-level statistics and “gut feeling” a better alternative.
When mentioning Big Data, the general perception is that structured, integrated, reliable and accessible data processing is far from become reality.
But why so much resistance? Big Data is a good… mainly because it’s Big. It’s actually astonishingly huge and with lots and lots of Terabytes and Petabytes of unused and mostly unstructured raw information which correctly managed could revolutionize pretty much any life context!
A few years ago I attended a meeting held by my pharmaceutical company’s headquarters in NY. For privacy reasons I cannot fully disclose its name but suffice to say that they created one of the most famous diamond blue pills in history that revolutionized the quality of life of a consistent number of men (and women). In that occasion they mentioned a few new ideas that were on the plate for the future of healthcare, the main one being how to start exploiting the potential of Big Data. The idea was to eventually reach a world where most of the population’s genetic and historical family disease track record could be stored and accessible, allowing to predict potential or future illnesses and shifting the focus of healthcare from curing diseases to preventing them. This project was founded on the common assumption in the bioinformatics community that in the last years, more scientific data has been generated than in the entire history of mankind.
Call it predictive analytics, statistical tools or algorithms, healthcare companies are understanding that without Big Data analysis, detecting the causes of diseases, targeting drug candidates and running more efficient clinical trials cannot not be fully achieved.
Thorough analysis on clinical and genetic Big Data can serve pretty much any purpose. For example predicting the probability of success of the hugely expensive and easily fallible clinical trials. Leading the companies to invest on projects that have more success potential or that better target certain patients does not only translate into higher profits for the firms but mainly to more and better drugs being approved for us!
"Combining larger datasets on drug response with genomic data on patients could steer therapies to the people they are most likely to help. This could substantially reduce the need for trial-and-error medicine, with all its discomforts, high costs and sometimes tragically wrong guesses.” (Kaufmann Foundation)
Thorough analysis on clinical and genetic Big Data can serve pretty much any purpose. For example predicting the probability of success of the hugely expensive and easily fallible clinical trials. Leading the companies to invest on projects that have more success potential or that better target certain patients does not only translate into higher profits for the firms but mainly to more and better drugs being approved for us!
"Combining larger datasets on drug response with genomic data on patients could steer therapies to the people they are most likely to help. This could substantially reduce the need for trial-and-error medicine, with all its discomforts, high costs and sometimes tragically wrong guesses.” (Kaufmann Foundation)
However, no matter how much better our life could become thanks to this new technological frontier, the eternal question of how to prevent security issues that the leakage of such sensitive personal information could cause should always be out there. If in the wrong hands or if of low-veracity, this data may be manipulated to push unnecessary but profitable treatments as well as provide the incorrect medical solutions to specific diagnoses.
Great post Valentina! An interesting application of big data in the pharma and genetics industry as you mention above will come from the efforts of human genome research companies. Companies such as 23 and me (https://www.23andme.com/) are now offering a full DNA analysis for under $100! By building up a huge database of its users, this company will have access to data about the human genome which will be very valuable (especially to pharma companies as you have discussed). Of course, as you also mention it has a number of serious security and privacy risks involved as well and these kinds of genome and health companies must tread carefully when exploring the potential of this area.
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