Biomedical R&D and other organizations generate massive volumes of complex data every year, ranging from high-throughput omics to longitudinal clinical trials. Managing and utilizing this data effectively is essential for developing new therapies and driving scientific research and innovations.

However, managing these assets presents significant challenges. Often, data is stored in disconnected, unstructured repositories. This fragmentation can slow down the research process- tracking clinical trials becomes difficult, building patient-centric models requires extensive manual intervention, and historical data is frequently left unused because it lacks the necessary context and harmonization. Polly Atlas is built to address this infrastructure gap.

Our Solution- A Structured Repository Designed for Life Sciences

Polly Atlas is a high-performance, structured data repository engineered specifically for the complexities of Life Sciences R&D.

Currently, teams often have to choose between the intuitive flexibility of spreadsheets, which struggle to scale, and traditional data lakes or relational databases, which can be too rigid for evolving scientific schemas.

Polly Atlas bridges this gap. It combines the ease of use of a spreadsheet with the scale, data integrity, and sub-50ms query speeds of a modern relational database. By utilizing collections of tables built on user-defined schemas, teams maintain control over how their data is structured and connected. This allows researchers to seamlessly store, link, and retrieve large-scale molecular and longitudinal clinical datasets in a single environment.

Key Capabilities of Polly Atlas

Polly Atlas is designed to move data from its raw state to an analysis-ready format by providing:

• Instant Harmonization: Transforms heterogeneous data types (omics, assays, clinical) into a unified, connected schema.
• High-Speed Retrieval: Delivers sub-50ms latency, allowing users to query complex datasets across millions of samples efficiently.
• AI-Ready Infrastructure: Provides data in a format optimized for immediate machine learning training and predictive modeling, reducing prep time.
• Unified Patient Journeys: Seamlessly links structured metadata with unstructured Real-World Data while maintaining HIPAA compliance.

The Workflow Framework

The efficiency of Polly Atlas is supported by an end-to-end pipeline that handles data preparation from ingestion to analysis:

1. Multi-Modal Data Ingestion: Accommodates diverse sources, including Omics, Clinical, Patient, Imaging, and Non-Omics Assay data.
2. LLM-Powered Harmonization Engine: Utilizes a framework for automated data processing, metadata curation, and quality checks to ensure data integrity.
3. Analysis Data Model: Intelligently fits data into a structured model stored on the Polly platform for reliable access.
4. Custom Visualization: Allows direct export of harmonized data into custom dashboards for clear exploration.

Measured Impact in R&D

Organizations implementing Polly Atlas have reported measurable improvements in their workflow efficiency, including:

• 7X faster time to analysis.
• 75% faster matching of indications to targets.
• 1000+ hours of manual data wrangling saved per project.
• 200+ multi-modal data products successfully delivered to Biopharma partners.

Practical Applications and Use Cases

Polly Atlas supports a variety of high-value R&D applications:

• Longitudinal Insight Integration: Combines metadata, treatments, and outcomes (including discharge summaries) in a single step.
• Accelerated Cohort Discovery: Uses flattened data models to help researchers quickly explore complex clinical data and identify relevant patient populations.
• Predictive Model Fueling: Leverages harmonized multi-site records to support ML training for diagnostic and prognostic applications.

Real-World Success

• Digital Biobank Transformation: An organization digitized and integrated clinical metadata from EMRs and PDFs into a queryable Atlas. This created a unified view that improved sample discoverability and longitudinal traceability across their teams.
• Automated Screening Workflows: By ingesting high-throughput drug screening data into an Atlas, a partner reduced their analysis time by 25X, while ensuring ongoing and historical records met FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.

These are just two of many real-world transformations. Polly Atlas helps transition siloed R&D assets into a highly organized repository, ensuring that your data is findable, linked, and ready for analysis.

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