Last week, I attended a great discussion on Big Data and life sciences, hosted by FierceBiotech and held in conjunction with the J.P. Morgan Healthcare Conference. The panel, moderated by FierceBiotech Editor-in-Chief John Carroll, included Google’s David Glazer, Broad Institute’s Anthony Philippakis, Foundation Medicine’s Michael Pellini and others. Each offered insights on the sector’s Big Data challenges and opportunities, and thoughts on how to store, manage and analyze biopharma’s data sets.
All agreed that the life sciences industry is “late to the party” of big data, and that re-engineering systems and processes to safely extract value from today’s new data streams will continue to be an enormous challenge. If the last few decades were centered on automating inefficient, paper-based clinical trial systems, the next twenty years will likely see those systems torn apart in an effort to create new pathways for improved, real-time connections between all stakeholders. As the panel discussed, there’s no shortage of data sources; what’s lacking are the relevant connections between clinicians, payers, patients, and pharmaceutical companies.
Philippakis offered that biology analysis needs a new infrastructure, and that new infrastructure must operate at a massive scale. He contends, like other industries, this can be done effectively and efficiently in a SaaS model.
One of my favorite observations came from an audience question: If “data moves at the speed of light, but medicine moves at the speed of life,” how can companies overcome this lag? How too, can we extract value from these new data sets, because simply managing them isn’t enough?
On machine learning, and its role in understanding the unstructured clinical trial data for academic and research purposes, Atul Butte, Director of the UCSF Institute for Computational Health Sciences discussed how they are able to use data intelligently on the 14.1 million patients who have come through one of the UC medical campuses for care and treatment. Everyone agreed that as we move toward personalized medicine, data can be used to guide patient care and improve outcomes.
Those of us with next-gen Big Data solutions are eager to help the sector overcome Big Data challenges and barriers; to provide better, faster insights and reveal new drug development opportunities, and ultimately help pharma deliver much needed therapies to the people who need them most.