The digital transformation spurred by big data is reshaping industries of all kinds. The life sciences sector is no exception.
In the past, it could take several years and billions of dollars to sequence one human genome. Today, the same task can be accomplished in under 24 hours and for less than $1,000.
Laboratory technologies vital to work in these fields have also evolved at a rapid pace. For example, the latest tools in life science research have allowed scientists to create complex three-dimensional visualizations of protein molecules. Scientists use these visualizations to assist in drug discovery and pioneer breakthroughs in personalized medicine.
The core driver of these game-changing advancements is big data. The volume and complexity of data continues to grow at staggering rates, especially in fields like genomics, bioinformatics, predictive biology, and chemistry.
Laboratory instruments also produce significant amounts of data—sometimes as much as one terabyte every hour.
Of course, with bigger data comes bigger data storage needs. The IT infrastructure behind life science databases will need to evolve with the growing scale and dimensionality of data.
With the ocean of information scientists are dealing with, they’ll also need modern software tools so they can quickly visualize and interact with big data. Otherwise, life sciences organizations will be data rich but information poor.
Broader Benefits of Big Data in the Life Sciences Industry
As long as life sciences organizations continue to upgrade their IT infrastructures to better integrate big data into their core processes, incredible benefits will continue to rise out of their work, including:
- Improved Risk Assessment: Big data can help scientists garner insights that reveal potential health hazards, evaluate the degree of risk associated with them, and help them arrive at conclusions to resolve the issue.
- Personalized Medicine: As scientists and doctors are able to more effectively process and understand electronic personal health information, they’ll be able to make more accurate judgments about what kind of medicine each patient needs.
- More Effective Assessments in Clinical Trials: Currently, most medicine uses the “standard of care” model, where general group responses and clinical trials are used to influence the provision of care. But when big data is leveraged effectively, more precise assessments can be made about how the medicine affects specific types of patients, including side effects that might otherwise go unnoticed. This can contribute to continued breakthroughs in drug discovery, among other things.
Legacy IT Infrastructure Bottlenecks Potential Breakthroughs
While the benefits that big data can bring to life sciences sound promising, organizations will need IT infrastructure and data storage capabilities that can keep up with the increasing demands.
According to a 2019 Deloitte/MIT study, only 20% of biopharma and life sciences organizations are maturing fast enough digitally. Unfortunately, legacy storage systems are bottlenecking performance and preventing applications from receiving the data that scientists need to make more breakthroughs.
New life science instruments and technologies are emerging. They have the potential to create new research opportunities that will lead to greater insights. But organizations will need to contend with several IT challenges first to be able to take advantage of these innovations.
Challenge #1: Data Silos
Many organizations are struggling to break out of data silos. Silos make it time-consuming and difficult for researchers across departments to access the data their work depends on. Silos develop, in part, because data gets stored in multiple tools and software solutions rather than in a centralized life sciences database.
To break down the barriers created by data segregation, organizations will need to find a way to smoothly transition data systems to a user-friendly software tool.
Challenge #2: Scale
As mentioned earlier, the latest scientific technologies produce a considerable amount of data. Life sciences organizations will need to scale their storage capacities to accommodate this influx of information.
If the appropriate scale isn’t reached in time, the workflows that rely on data captured by these instruments will be disrupted.
Challenge #3: Big Data Analytics
Big data is only as useful as the insights you are able to extract from it. Whether you’re running a big data analytics pipeline to power personalized medicine or leveraging AI to assist in drug discovery, high-performance computing (HPC) requires high performance data storage. Making the switch from spinning disk drives to low-latency and high IOPS all-flash storage arrays is now a necessity.
