We live in an era of digital revolution. The result? Big Data. Typically, the word ‘Big Data’ refers to data sets so large and complex that they are difficult to process using traditional applications, it has the potential to impact almost everything around us. Travel, shopping, banking, education, sports, and Life Science is no exception.
Life science companies have been early adopters of Big Data because of the rapid generation of large and complex biomedical data created by devices across the globe every day. Even smaller machines are capable of producing piles of data sets, and many scientists are concerned that over the next 10 years, the magnitude, diversity and the dispersed nature of this data deluge will escalate, making it increasingly difficult to find relevant data and to derive meaningful patterns and insights. Now, think of its impact and the challenges accompanied by the detonation of biomedical data accumulated in computers and servers around the world. As a matter of fact, the management of data presents increasingly difficult issues, starting from data privacy to infrastructure that is needed to securely generate, maintain, transfer and analyze data in enormous volumes in largely disconnected environments.
As data will continue to play a critical role in the future of life science companies, especially in research and development (R&D), finance, marketing, and risk management, experts and researchers seek to find the answer to a fundamental question: How do we harvest and leverage data to gain a competitive advantage?
While most life science organizations seek new ways to gain competitive advantages in the marketplace, many experts believe no single approach is optimal for all analyses. They believe a combination of heterogeneous computing and cloud computing is emerging as a powerful new option, as it meets three critical needs for life sciences computing: crunching more data faster, flexibility to shift from one architecture to another in a secure public or private cloud environment and increasing access to the accelerated performance.
This new approach provides so many other benefits for cloud computing, such as lower costs (pay as you go), reduced IT support, ease to adapt technology upgrades, rapid scalability and dedicated 24/7 public or private cloud service to a single organisation, in the places of greater cost, integration, and management challenges. This architecture can provide even small research groups with affordable access to diverse computing resources. Besides, it also allows the research and clinical collaborators worldwide to work together and establish statistically significant patient cohorts and use expertise across different institutions. Data management and resource utilization across departments in shared research HPC cluster environments, analytics clusters, storage archives, and external collaborators become easy and affordable. It can even address critical shortages in bioinformatics expertise and burst capacity for high-performance computing (HPC) clusters.
Now, there is a confounding aspect in life sciences and healthcare i.e most biologists, physicians, and other users are usually not IT experts. Although some life scientists have significant computational skills, others do not understand computer semantics enough to know that in the tech world, Python is not a snake and Perl is not a gem (they are programming languages). For them, it’s challenging enough to choose the best tech solutions. Even today, a vast majority of bioinformatics and healthcare applications runs on standard clusters, but luckily, the trend is changing, as many research organizations have already hit technical and financial roadblocks which prevent the obtaining of sufficient HPC resources for analyzing all the output of high-data-rate experimental instruments.
There will be no shortage of challenges when companies begin to adopt Big Data strategies into their operations. However, the benefits of Big Data that come with an effective, robust and holistic Big Data management strategy are too great for any company to ignore. Many life sciences organizations who were early adopters of Big Data as a core asset of their operations have already staked out a distinct advantage in the marketplace, reaping tangible benefits that include:
1. A secure environment for approved researchers to analyze anonymized patient and clinical data, from a variety of sources, in a controlled and auditable manner.
2. Several million dollars of savings in R&D and new hire training by reducing knowledge workers.
3. Improved and unified sales and marketing process in multiple geographies through an integration of clinical data from multiple healthcare data providers.
Big Data isn’t just hype; it’s here. Companies know they can’t escape Big Data, though perhaps can hide for now. They have already taken steps to understand their use cases — the questions that were buried in Big Data for decades.
This article recently appeared on the eleventh edition of our content partner Big Data Innovation Magazine. Published with permission.
