The Role of Data Integration in Scientific Discovery and Innovation

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

Register for this webinar to learn about:

• 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

Speaker

Joel Melby, Data Analyst, TetraScience

Joel champions and facilitates key integrations across the laboratory ecosystem, enabling the seamless flow of data between data sources (data generators) and targets (data consumers). Before joining TetraScience, Joel held scientific and technical roles at GSK, where he realized a passion for data-driven decision-making. Through automating data analysis and visualization, he was able to accelerate key decisions during his time at GSK, resulting in faster experimental cycle times. Joel earned his Ph.D. in Chemistry from the University of Illinois, where he studied natural product biosynthesis.

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