Tetra Data

Make your data compliant, harmonized, liquid and actionable

Are you using your scientific data to its full potential?

Organizations encounter several common challenges accessing and using their raw scientific data:

  • Multiple vendor-proprietary formats
  • Difficult to search / retrieve
  • Lack of context and traceability
  • Not reusable with informatics and analytics applications

Only Tetra Data is compliant, harmonized, liquid, actionable, and FAIR (findable, accessible, interoperable and reusable).

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All scientific data centralized in the cloud

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Enriched with metadata to provide context

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Harmonized for comparison and reuse

What is Tetra Data?

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Raw or primary data

Collected from instruments, informatics applications in R&D, Manufacturing, and QA/QC

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Enriched context

Tagged with metadata to provide scientific context and enable faster and easier search

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Traceable provenance

Stored with an audit trail that provides complete history of all changes to raw and processed data

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Harmonized content

Standardized to allow comparisons across data sets, easy access from informatics applications, and reuse by advanced analytics

Tetra Data provides the essential properties needed by Life Sciences organizations to leverage the full potential of their scientific data


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

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Tetra Data supports the entire scientific data journey

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.

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1. Collect

Organizations often begin with immediate essentials: ingesting and storing scientific data in the cloud.

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2. Enrich

Next, data is enriched with metadata to provide context and meaning, enabling searchability and auditability.

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3. Harmonize

As a final step, data is cleaned, transformed, and harmonized for software consumption, visualization, and higher-value analytics.

Resources to Explore