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SmartLab Exchange EU 2021

Event Status:

Past Event

Event Description

We joined thought leaders in Biotech and Pharma space for a two-day a virtual event focused on R&D/R&D IT, Manufacturing and QA/Q. SmartLab Exchange is an intimate online environment, creating connections which become long-term relationships. The event will include inspiring keynote addresses, in-depth case studies, structured networking and interactive roundtable groups.

Our Chief Scientific Officer, Mike Tarselli, Ph.D., led a roundtable discussion on how thought leaders in the space are keeping up with data velocity and rapid scientific innovation. Check out the session abstract below!

The R&D Data Cloud: A Paradigm Shift for Biopharma

Let's face it: Many scientific organizations have 21st-century research being held back by '90s (or older!) information systems. As innovation and discovery continues to accelerate at an exponential rate, roadblocks like inconvertible formats, obsolete and diverse instrumentation, on-prem legacy systems and hundreds of different file extensions still haunt simple queries like "Show me all data we have attached to [Sample X]." At TetraScience, we know there's a better way; we ask: “How do you keep up with data velocity and rapid scientific innovation?”

This session will be a lively Q&A / roundtable on the following topics:

  • Where in the journey to “Industry 4.0” is your organization with regards to AI/ML, automation, sensors, and Cloud adoption? 
  • What are your biggest daily data challenges? Handling the scope/scale of Big Data (4 V’s: Volume, Velocity, Variety, Veracity)? Decision-making? Adoption of data-centric processes? (Something else?) 
  • How has your organization handled “new modality” workflows - mRNA, biologics, CRISPR, PROTACs - that have not been standardized to the same level as small molecule workflows of the past ~20 years? 
  • Which instruments, techniques, or processes do you envision causing you headaches in the next 5 years and how are you future-proofing investments within your organization?
  • What changes need to happen to enable artificial intelligence past it being just a buzzword? How is your organization building the correct data infrastructure or "right" information to truly enable AI/ML?
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