Why Biopharma Needs an End-to-End, Purpose-Built Platform for Scientific Data — Part 2

March 30, 2022

In part one of this series (Why Biopharma Needs an End-to-End, Purpose-Built Platform for Scientific Data — Part 1), we discussed some of the reasons do-it-yourself data platform projects often fail to deliver promised benefits, or do so at an unexpectedly high cost.

To review: building a do-it-yourself data solution from horizontal components means assuming responsibility to select, integrate, and manage all the pieces and parts across the lifecycle. Not to mention researching, architecting, building, and maintaining all of the integrations. That's a tall order, requiring headcount and specialized skills — most of which are focused on building, operating, and getting data into the platform (and much less focused on extracting value from data itself).

Making matters worse, there's an "impedance mismatch" between the capabilities offered by generic data components and services (e.g. ingestion, transformation, cloud storage and search, etc.) and biopharma infrastructure, workflows, regulatory requirements, and scientific characteristics and requirements. A do-it-yourself project that aims to create a solution able to gain insights from scientific data requires scientific and process knowledge to extract, parse, and enrich data with context, and also must map data into a schema that makes them readily findable, accessible, interoperable, and reusable (FAIR).

The result: do-it-yourself efforts consume vast resources and add non-strategic work across the organization, while delivering a sub-par solution built from brittle, inflexible, and hard-to-maintain integrations between data sources and targets. Even though the data may be aggregated, they are devoid of scientific context and not harmonized into a common format, making them hard to find, use for automation, analyze, visualize, or target with AI/ML.

Tetra Data Platform: Unified and Purpose Built

Meeting these challenges requires a different approach. Tetra Data Platform (TDP) represents a fundamental shift: bridging data sources and data targets to accelerate research, development, manufacturing, and the wider operational and business strategy of life science organizations.

Data-centric: treating data as the core asset, providing stewardship of data through its entire lifecycle. Tetra Data Platform is built to manufacture and manage Tetra Data. Tetra Data represents a vendor-neutral model for scientific data created to support the data access, instrument and application integration, compliance, and data analytics and visualizations. With Tetra Data, organizations can now effectively use their data to accelerate innovation.

Tetra Data is:

  • Compliant. Tetra Data simplifies regulatory compliance through a complete audit trail, providing visibility into changes to data or configurations and data provenance, improving data integrity, governance, and security.
  • Harmonized. Tetra Data combines data formats produced by instruments and informatics applications from multiple vendors into a standardized format in a centralized location – enriching raw data with scientific context by adding metadata. By harmonizing Tetra Data into a single schema, information becomes easier to find, extract and transform for use by analytics, visualization, AI/ML.
  • Liquid. While harmonized Tetra Data makes it easy to construct new dataflows by eliminating manual processes to incorporate new technologies, Liquid Tetra Data seamlessly flows to provide access to data on the instruments and applications where it is needed, robustly and at scale.
  • Actionable. Tetra Data – ready for consumption by analytics, AI/ML, and other insight-generating technologies – turns data into an asset that drives business and scientific decision-making.

Tetra Data Platform is architected to keep data as its focus through its entire lifecycle. It provides:

  • A flexible ingestion tier (agents, connectors, IoT proxies, etc.) that robustly manages connectivity and access to any kind of data source
  • A sophisticated pipeline architecture, engineered to facilitate rapid creation of self-scaling processing chains (for ingestion, data push, transformation, harmonization) by configuring standardized components, minimizing the burden on coding and operations while reducing cloud costs associated with data processing
  • A high-performance, multi-tiered cloud storage back end, enabling storage to scale on demand while minimizing storage costs
  • A life-sciences-focused, fully-productized, plug-and-play, distributed integration architecture that runs across the purpose-built platform. Integrations are engineered by the TetraScience IT and biopharma experts (in collaboration with our ecosystem of vendor partners) to extract, deeply parse, and fully enrich (e.g., with tags, labels, environmental data, etc.) data as they emerges from sources and harmonize them into an open schema to make them FAIR
  • Open, modern REST APIs (including apps built upon the API) and powerful query tools provide easy access to raw data and harmonized Tetra Data for automation, analytics, AI/ML applications, and popular data science toolkits (e.g., Python + Streamlit)

This data-centric architecture ensures that:

  • Appearance of new data from instruments and applications (and the readiness of instruments and applications to accept new instructions) can be detected automatically, enabling hands-free automation including ingestion, enrichment, parsing, transformation, harmonization, and storage on the inbound side, plus search/selection, transformation, and push (or synchronization) on the outbound side
  • TDP enriches, parses, harmonizes, and stores data as it becomes available, preserving context and meaning for the long term, ensuring provenance and traceability. This information makes Tetra Data immediately useful for analytics and data science in close to realtime (i.e., while experiments are running)
  • Harmonized data are stored in JSON data structures that are fully documented and completely open, making them searchable and comparable, facilitating rapid (automatic) ingestion by applications

Cloud-native: TDP incorporates best-of-breed open-source components and technologies (e.g., JSON, Parquet) and popular standards favored by scientists and by life sciences and data sciences professionals (e.g., SQL, Python, Jupyter) in an aggressively cloud-native architecture that ensures easy, flexible deployment, resilience, security, scalability, high performance, and minimum operational overhead, while optimizing to provide lowest total cost of ownership.

Life sciences-focused, with connectivity, integration and data models purpose built for experimental data at the core: TetraScience has created a large (and growing) organization, deeply skilled in life sciences and technology; and has evolved a mature process for identifying, building, and maintaining a library of fully-productized integrations with biopharma data sources and targets and creating models for common data sets. These integrations are purpose built and tailored to fulfill informatics and data analytics use cases in life sciences. 

Open and vendor agnostic, leveraging a broad partner network: TetraScience has partnered (and actively collaborates) with the industry’s leading instrument and informatics software providers as part of the Tetra Partner Network (TPN). As TPN and our collective ecosystem grows, it benefits all network members (and TetraScience customers). This partnership between TetraScience and leading solution providers significantly accelerates integration development and productization, helps ensure integration quality, keeps integrations in sync with product updates, and helps guarantee that integrations fully support high-priority, real-world customer use cases.

Conclusion

Biopharma organizations can best exploit their most important asset — scientific data — by implementing a purpose-built, end-to-end solution that's data-centric, cloud native, life sciences focused, and open. Hewing closely to these principles, Tetra Data – and the Tetra Data Platform – helps reduce non-strategic organizational spread, enabling dedicated data experts to manage data processing and data modeling, including configuring, managing, and tracking dataflows from end to end. Meanwhile, scientists and data scientists can enjoy a more self-service, unified data experience.