Trusted Data Lakehouse

Lifebit's Trusted Data Lakehouse™ (TDL) is a purpose-built biomedical data platform designed to transform raw, diverse health datasets into searchable, harmonized, and analysis-ready data products. It unifies clinical, multi-omics, imaging, NGS, EHR, and sensor data into a single accessible environment, supporting the full data lifecycle from retrieval and quality control through to cataloging and research-ready delivery. The platform is built for data administrators, biomedical researchers, pharmaceutical organizations, and health data providers who need to accelerate insights while maintaining strict data governance and regulatory compliance.

Operating as a federated lakehouse, Lifebit TDL keeps data within each provider's own environment, enabling secure, permissioned access across distributed sites without requiring centralized data movement. Trusted by leading pharma companies and data providers, the platform has demonstrated proven efficiencies across a global data network of over 250 million patient datasets, delivering up to 90% cost savings on data product creation and enabling multimodal biomedical data to be fetched, linked, QA'd, harmonized, and cataloged in as little as two hours.

Core Capabilities

• EHR and NGS data retrieval: Connects to and retrieves data from major EHR systems including Epic, Cerner, and Meditech, as well as NGS providers such as Tempus and Foundation Medicine, and on-site sequencing facilities, across extensive U.S. and global healthcare networks.
• Flexible data integration methods: Supports multiple retrieval protocols including API, Batch, FHIR, and direct database connections, allowing integration tailored to each organization's existing infrastructure.
• Multimodal data support: Handles a wide range of structured and unstructured biomedical data types, including FASTQ, VCF, clinical records, imaging, and multi-omics datasets.
• Advanced data harmonization: Uses AI-driven pipelines and industry-leading tools — including DRAGEN, Parabricks, Sentieon, and GATK — to harmonize complex multimodal data into standardized common data models such as OMOP for EHR and clinical data, and VCF for genomic data.
• Data quality assurance and cleaning: Performs QA/QC, deduplication, de-identification, and data linkage to ensure high-quality, research-ready datasets.
• Data cataloging and discoverability: Catalogs harmonized data to establish a single source of truth, making datasets searchable and readily discoverable across the organization.
• Cohort building and analytics: Enables users to build custom cohorts within seconds from harmonized datasets, with access to advanced analytics including GWAS, VEP, and PRS, and support for tools such as JupyterLab and RStudio.
• Real-time data retrieval and analysis: Supports time-sensitive research needs with real-time data retrieval and automatic harmonization for immediate use, depending on the retrieval method selected.

Compliance and Data Governance

• FDA real-world evidence compliance: The only lakehouse to meet stringent FDA RWE guidelines, providing complete data lineage and provenance with every retrieval and maintaining 100% compliance with FDA data lineage and retrieval regulations.
• Full audit trails: Every step in the data lifecycle is tracked with acquisition timestamps, retrieval methods, and provenance details, giving users full transparency and supporting regulatory submissions.
• Privacy-preserving technologies: Built-in security measures and controlled, permissioned access ensure data remains protected within each provider's environment.
• Secure Airlock™ protocols: Any data exports are subject to review and approval processes to prevent unauthorized data movement.
• Federated architecture benefits: Data sovereignty is maintained at source, reducing risks associated with data movement, lowering infrastructure and maintenance costs, and enabling compliant cross-site querying without centralization.

How It Works: Step-by-Step Workflow

1. Create the organisation and workspace: Set up a new organization or select an existing one, then create the primary workspace where data access is managed. Administrators configure essential infrastructure details such as AWS settings to ensure secure integration.
2. Connect and set up existing data sources: Integrate data from multiple EHR systems, NGS providers, and other biomedical data sources using the platform's flexible retrieval methods.
3. Perform QA, data cleaning, and harmonization: Apply quality assurance, deduplication, de-identification, and AI-driven harmonization pipelines to prepare data for use.
4. Catalog data to establish a single source of truth: Organize and catalog harmonized datasets to make them searchable and discoverable across the research environment.
5. Assess data for study readiness: Evaluate datasets to confirm they meet the requirements for specific research studies before analysis begins.
6. Collaborative and secure research, discovery, and translation: Researchers access cataloged, harmonized data within a Trusted Research Environment to conduct analyses, build cohorts, and generate insights.

Lifebit's Trusted Data Lakehouse™ is deployed in a federated model, ensuring data remains within each organization's own environment while enabling seamless cross-site collaboration. The platform integrates with major cloud infrastructure (including AWS), supports a broad ecosystem of genomic and clinical data tools, and is designed to meet the compliance requirements of pharmaceutical companies, health data providers, and research institutions operating under FDA and other regulatory frameworks.