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The rapid development of COVID-19 vaccinations demonstrated how artificial intelligence and machine learning are powerful tools to speed the drug development process.
As the pandemic wanes, these technologies continue to drive a new wave of breakthroughs in healthcare, with new drugs and improved treatments directly benefiting individual patients and leading the way toward a healthier future for humanity. In a report on “The Next Normal,” global management consulting company McKinsey & Co. identified a trend toward integrating AI into research workflows and predicted “a world in which scientists will be able to … generate new insights at an unprecedented pace.”
For that to happen, health sciences organizations need to move toward new methods for efficient and secure data sharing. The traditional method of sharing via File Transfer Protocol requires creating copies of data and results in a loss of governance once the copy is handed off to the third party.
“What we’re starting to see now with some of our customers is live sharing of information,” said Jesse Cugliotta (pictured, right), global industry go-to-market lead of healthcare and life sciences at Snowflake Inc. “It’s become very common over the last couple of years. And I think a big part of it is having the right technology to do it effectively.”
Cugliotta and Nicholas Taylor (left), executive director of informatics at Ionis Pharmaceuticals Inc., spoke with theCUBE industry analyst Dave Vellante at the Supercloud2 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the future of data and cloud within healthcare and pharmaceutical research. (* Disclosure below.)
Real-time sharing of data sets provides two important benefits, according to Cugliotta. First, there is no physical duplication of data, with consumers able to interact with data without moving it from its original location.
“It’s a much more secure way to work with a colleague if you don’t have to copy your data and potentially expose it,” Cugliotta said.
Secondly, because they are accessing the original data, the consumer is querying it in a real-time state and accessing the most up-to-date results.
Snowflake customer Ionis Pharmaceuticals is one organization taking advantage of this ability. Being a Snowflake customer is enabling Ionis to directly take in data feeds from one of its contract research organizations.
“Historically, this clinical trial data comes in on an FTP file, we have to process it, take it through the platforms, and put it into the warehouse,” Taylor said. “But one of the CROs … is a Snowflake customer … so they’re exposing their tables of data that [previously] came in these FTP files directly into our Snowflake instance.”
Being able to bring terabyte-size datasets straight into Snowflake and run analytics on them is a major asset for Ionis’ small information technology shop, according to Taylor.
“We don’t have to worry about maintaining those jobs that take those files in,” he said. “We don’t have to worry about the jobs that take those and shove them on the warehouse. We now have a feed that’s directly there that we can use a [data build] tool like dbt [Labs] to push through directly into our model.”
The power behind AI and machine learning comes from the data it ingests, with high-quality, real-time data essential to build accurate models. Live sharing of data provides this, but it is important to maintain data governance throughout the process. Ionis has been looking into ways to link sets of highly regulated genetics data using Snowflake’s Distributed Data Clean Rooms.
One organization has the actual genetics analysis, while another has the metadata of age, ethnicity, location, etc., that pertains to the first set, Taylor explained.
“Being able to build a data white room so we can put that genetic data in a secure place, anonymize it and then share the amalgamated data back out in a way that’s able to be joined to the anonymized metadata, that could be pretty huge for us,” he said.
Accessing real-time data is essential for advanced analytics. It also brings benefits for use cases such as supply chain planning, according to Cugliotta.
“If I can see the actual inventory positions of my clients, of my distributors, of the levels of consumption at the pharmacy or the hospital that gives me a lot of indication as to how my demand profile is changing over time versus working with a static picture that may have been from three weeks ago,” he said.
This is increasingly important as supply chain constrictions continue, with accurate demand forecasting making the difference in being able to get products to customers when and when they need them. This not only impacts levels of customer satisfaction, but in the case of medical supplies can be a matter of life or death.
Being able to seamlessly share datasets regardless of the cloud provider on which they sit opens new opportunities for research and development. Ionis can access a multi-terabyte dataset from data intelligence provider Compile Inc., even though Compile is an Amazon Web Services Inc. customer on the East Coast of the United States, and Ionis uses Microsoft Azure and is on the West Coast.
“It’s hugely beneficial that Snowflake supports this kind of infrastructure,” Taylor said. “From my point of view, the data just exists. We don’t have to jump through hoops to download it here and then re-upload it here. They already have the mechanism in the background to do these multicloud shares.”
Enabling these underlying sharing mechanisms to abstract away the complexity of multicloud is one of the goals of Snowflake, according to Cugliotta. “The whole concept should be to bring the logic to your data versus your data to the logic,” he said.
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Supercloud2:
(* Disclosure: This is an editorial segment. TheCUBE is a paid media partner for Supercloud2, but sponsors for theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)
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