• The benefits of findable, accessible, interoperable, reusable (FAIR) data in the cloud
• The importance of multi-point productized integrations
• The steps to automate the scientific data lifecycle

Why End-to-end Data Lifecycle Automation Is Critical

With an automated end-to-end data lifecycle, harmonized scientific data flows freely throughout the biopharma data pipeline to accelerate innovation and support quality. Decoupling instrument control from data publishing enables scaled-up data science automation.

We discuss these benefits in detail in our white paper:

• With an automated and remote control interface to interact with instruments, organizations don't need to worry about collecting data with the heterogeneous and fragmented nature of lab data systems.
• With automated data acquisition across different instruments and lab systems, scientists no longer have to manually locate, transfer, and clean the data.
• With data pipelines for automated processing and interactive analysis, the data is automatically prepared, analyzed, and presented to the researcher, driving faster decision-making.