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SmartLab Exchange USA 2021

Event Status:

Past Event

Event Description

As organizations across industries such as pharma, biotech, chemicals, cosmetics, and food & beverage seek to redefine the way research and labs operate, it is crucial that they are able to keep their finger on the pulse regarding new technologies, systems, and processes to increase the speed of innovation. In order to truly capitalize on the promise of tomorrow’s lab, organizations are tasked with managing, standardizing, and analyzing a complex volume of data to inform decision making, building greater connectivity throughout the lab through automation, as well as harnessing digital technologies to unlock new ways of working and unshackle the scientist from the bench.

We joined thought leaders in the space who are spearheading innovative laboratory strategies across the R&D/R&D IT, Manufacturing and QA/QC space for two days packed with idea-sharing and informal networking, arming attendees with the stimulus they need to drive forward innovation within their respective organizations. 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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