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The Role of Data Integration in Scientific Discovery and Innovation

Event Status:

Wednesday, September 22nd, 2021 | 11:00am EST

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Event Description

The ability to reproduce and automate science fuels innovation in life sciences R&D, enabling researchers to apply higher-quality data science and answer more challenging scientific questions. 

However, achieving these goals at scale can be fraught with pitfalls and roadblocks. Instrumentation and cloud systems speak different languages, resulting in massive amounts of time is wasted wrangling data and waiting for experiments to complete, and data is routinely incomplete or inaccurate, slowing any sort of discovery. At its core, this is a complex data integration problem.

What do life sciences organizations stand to gain by solving this problem? They can benefit from:

  • Reduced costs and risks associated with manually connecting instruments and software or relying on point-to-point integrations
  • Improved scientific data integrity
  • End-to-end data automation and repeatable scientific workflows
  • A near-real-time Make-Design-Test-Analyze (DMTA) feedback loop between any given experiment and the design of the experiment (DOE) 
  • Faster iteration of the above, leading to rapid innovation cycles

How can your organization solve this problem? The possibilities are endless when scientific research leans on the power of a “Dynamic Duo” in R&D: turn-key integrations that incorporate the state-of-the-art in data engineering, life sciences, and data science disciplines, and an Intermediate Data Schema (IDS) that harmonizes disparate data and enables advanced data science applications. 

Join us for a webinar, where you will learn:

  • The defining criteria of a turn-key data integration solution, how these battle-tested, production-ready solutions drive efficiency gains, and how they foster a scientific data ecosystem
  • How the IDS harmonizes disparate data into a unified format for predictability, consistency, and efficient querying 
  • How this Dynamic Duo liberates experimental data, automates the full life cycle and flow of data, and enables cloud-based, distributed data processing and analytics at massive scale

For more information and to register for the webinar, visit the Xtalks Webinar page.

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