Life science processes, be it in research, drug discovery, development, manufacturing, or diagnostic test development and execution, have grown in complexity and scale, not the least because the industry is facing ongoing pressures such as ROI challenges. This rapid growth has resulted in disconnected and suboptimal laboratory process management systems and the lack of a unified data strategy that would provide the necessary laboratory and business process insights with the potential to shorten the overall product life cycle.

In parallel, the industry is heavily investing in modernizing data infrastructures and, more recently, in artificial intelligence (AI) capabilities to derive insights from the rapidly growing volumes of data generated.

While the promise of AI is huge, what is often overlooked when implementing generative AI and predictive AI models is the foundation of data on which AI is based and used. To derive high-value insights from an AI data model requires removing data silos and applying an enterprise-wide data standard.

The L7 Informatics and SciBite partnership offers the solution to these requirements. As contextualized data is collected during process orchestration within the Unified L7|ESP platform, the integration with SciBite’s CENtree platform facilitates the alignment of the data collected to enterprise data standards and public ontologies.

This webinar will outline the synergies between L7 Informatics and SciBite and how an integration of our respective platforms, support our mutual customers in preparing their data to fully exploit the power of AI.