Scientific data engineers in biopharma organizations manage significant challenges as they provide scientists and data scientists with the high-quality data they need. These challenges include:
- Consolidating data from complex workflows
- Navigating scattered, ad hoc file storage across massive companies
- Tedious, error-prone manual transcription processes
- Poor metadata attribution
This guide explains how The Tetra Scientific Data and AI Cloud™ solves these issues and empowers scientific data engineers to build and configure pipelines that enable downstream usage of informatics applications, provide centralized and searchable data storage, and power advanced analytics, visualization tools, and AI/ML.
