Organizations encounter several common challenges accessing and using their raw scientific data:
All scientific data centralized in the cloud
Enriched with metadata to provide context
Harmonized for comparison and reuse
Collected from instruments, informatics applications in R&D, Manufacturing, and QA/QC
Tagged with metadata to provide scientific context and enable faster and easier search
Stored with an audit trail that provides complete history of all changes to raw and processed data
Standardized to allow comparisons across data sets, easy access from informatics applications, and reuse by advanced analytics
with 21 CFR Part 11 / Annex 11 / GxP for data integrity and traceability
to seamlessly flow across instruments, applications, and departments
across vendors into an open format for interoperability and data comparison
for analytics and visualizations to provide insights and drive decisions
Tetra Data supports the path that Life Sciences organizations follow as they incorporate and mature their scientific data process. It begins with the integration of instruments and informatics applications, adding value through engineering of the data, gaining insights through data science and visualizations, and ultimately applying AI/ML for impactful data-driven decision making.
Organizations often begin with immediate essentials: ingesting and storing scientific data in the cloud.
Next, data is enriched with metadata to provide context and meaning, enabling searchability and auditability.
As a final step, data is cleaned, transformed, and harmonized for software consumption, visualization, and higher-value analytics.
