Our team attended the July 29, 2026 PHUSE webinar, “Building a Trustworthy RWD Analytic Platform: Key Considerations and Strategies,” presented by William Kuan and Ashwini Reddy from SAS. The session outlined what pharma, biotech, and healthcare organizations need to consider when designing modern architectures for Real-World Data (RWD) and Real-World Evidence (RWE).
As life sciences validation and software quality consultants, our core takeaway was clear: building a feature-rich RWD analytics platform is only half the challenge – validating it so outputs are reproducible, compliant, transparent, and audit-ready is what ultimately determines regulatory success.
Below is our perspective on the webinar’s key themes and how Kalleid supports validation of RWD analytics environments.
The Five Pillars of a Trustworthy RWD Platform (and Kalleid’s Validation Perspective)
- Centralized Governance, Traceability, and Lineage
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- Webinar Insight: A trusted RWD platform requires centralized governance to ensure transparency, reproducibility, and full traceability of the entire analysis process (from raw data to final analysis).
- Our Perspective: If an analysis cannot be reproduced or traced back to its source, it cannot withstand regulatory scrutiny (i.e., be submitted to agencies like the FDA).
- How We Validate: Kalleid designs risk-based validation plans aligned with GAMP® 5 and 21 CFR Part 11 to verify audit logging, version control, data lineage tracking, pipeline transformations, and rollback functionality.
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- Trustworthy, Explainable AI and Drift Monitoring
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- Webinar Insight: Black-box AI models are unacceptable for regulatory or clinical decision-making. Modern platforms must support explainability (e.g., LIME,SHAP), fairness/bias assessments, and continuous drift monitoring.
- Our Perspective: AI/ML validation is dynamic – models must be tested both at deployment and continuously throughout operation.
- How We Validate: Kalleid develops Operational Qualification (OQ) and Performance Qualification (PQ) protocols specifically for AI components. We verify explainability outputs, test bias thresholds, and simulate drift to ensure alerts and safeguards function correctly.
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- Cloud-Native Scalability and GxP Qualification
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- Webinar Insight: RWD involves massive, complex datasets requiring cloud-native scalability, high-performance in-database/in-memory parallel processing, and secure hosting environments.
- Our Perspective: Scalability is irrelevant if the underlying cloud infrastructure isn’t properly qualified.
- How We Validate: Kalleid performs Installation Qualification (IQ) and cloud environment qualification, auto-scaling, test strict Role-Based Access Controls (RBAC), confirm encryption standards (in transit and at rest), and verify multi-tenant or GxP-qualified hosting isolation.
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- Cross-Functional Collaboration and Low-Code UAT
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- Webinar Insight: RWD evidence generation spans epidemiology, data science, medical affairs, and commercial teams. Platforms must support both low-code/no-code interfaces and programmatic workflows so that all roles can collaborate without creating data silos.
- Our Perspective: Functional testing alone does not capture how diverse users interact with the system. Broad user adoption requires intuitive, verified workflows.
- How We Validate: Kalleid leads scenario-based User Acceptance Testing (UAT) with cross-functional stakeholders – from epidemiologists building cohort queries via drag-and-drop interfaces to data scientists writing custom code – ensuring both low-code and code-based workflows produce consistent, validated analytical results.
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- Openness, Multi-Language Support, and Interoperability
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- Webinar Insight: Open platforms must support multiple programming languages such as SAS, R, Python and emerging standards like Model Context Protocol (MCP) for LLM integration. They must connect seamlessly via APIs.
- Our Perspective: Multi-language flexibility increases risk if data handling differs across execution engines.
- How We Validate: Kalleid builds cross-engine integration tests to confirm data precision and integrity across languages, validate REST API integrations and LLM context hooks, and ensure third-party connections do not introduce security vulnerabilities or data leaks.
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How Kalleid Supports Validation of RWD Analytics Platforms
Whether implementing commercial software, building custom RWD environments, or integrating advanced AI tools, Kalleid delivers end-to-end computer system validation:
[System Risk Assessment]
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[URS / FRS Development]
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[IQ / OQ /PQ Scripting and Execution]
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[Cross-Functional UAT]
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[Audit-Ready Validation Package]
- Risk Assessment and Scoping: Mapping GxP (vs. non-GxP) boundaries across RWD ingestion, storage, and analytical layers.
- Requirements Definition: Developing clear User Requirement Specifications (URS) and Functional Requirement Specifications (FRS) tailored to RWD workflows.
- Execution (IQ/OQ/PQ): Writing and executing robust qualification scripts that test technical claims—from database scalability to AI explainability algorithms.
- User Acceptance Testing (UAT) Management: Creating real-world scenario scripts for biostatisticians, epidemiologists, and data teams.
- Validation Summary Package: Delivering a complete, traceable, audit-ready Validation Package – including Traceability Matrices and SOPs – that stands up to internal audits and regulatory scrutiny.
Final Thoughts
The SAS presenters emphasized that trustworthy RWD requires four foundational qualities: Reliability, Relevance, Transparency, and Reproducibility.
At Kalleid, we help life sciences organizations turn those principles into validated, compliant, and inspection-ready realities.
