FlowGenX

FlowGenX is an enterprise data lineage solution designed to provide comprehensive visibility over how data moves, transforms, and is consumed across an organisation's entire technology stack — from data warehouses through interconnected systems to end applications. Built with life sciences and regulated industries in mind, FlowGenX helps teams maintain clinical data integrity, meet regulatory obligations, and strengthen data governance across complex, fragmented pipelines.

FlowGenX is particularly well-suited for pharmaceutical, biotech, and clinical research organisations that must adhere to strict compliance frameworks such as FDA regulations, HIPAA, GDPR, and ALCOA+ principles. By delivering end-to-end traceability and automated lineage mapping, the platform enables data, compliance, and IT teams to reduce risk, accelerate audits, and unify data operations across labs, EDCs, CDMS, and analytics platforms.

Why Organisations Need Data Lineage

• Ensure clinical data integrity: Track data provenance across all study phases — from collection through to analysis — to maintain trust in trial outcomes and support reproducibility.
• Simplify regulatory compliance: Maintain complete, traceable data flows to streamline FDA and global health authority submissions and audits.
• Strengthen data governance and patient privacy: Monitor lineage paths and access controls to protect sensitive, patient-level data and meet HIPAA and GDPR requirements.
• Reduce compliance risk: Identify data discrepancies early and enforce ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate) across all connected systems.
• Unify fragmented data pipelines: Align source-to-target mappings across laboratories, EDCs, CDMS, and analytics platforms for seamless integration and operational efficiency.
• Improve pharmacovigilance oversight: Trace adverse event data from initial capture through to regulatory reporting to enhance safety monitoring and ensure timely submissions.

Key Features and Capabilities

• Track inbound, outbound, and bi-directional data flow: Select any node in the data lineage graph to instantly highlight all related data flows. Clicking on any child or leaf node reveals all connected nodes and edges for that component, enabling rapid root cause analysis, impact assessment, and compliance audits.
• Navigate nested child nodes and subsystems: Expand or collapse parent nodes to reveal or hide subcomponents without rerunning lineage tracing logic. Switch seamlessly between macro (overview) and micro (detail) levels of the lineage graph using MiniMap and zoom controls, and locate specific data elements or lineage paths using powerful built-in search tools.
• Switch between pre-configured flow models instantly: Toggle between multiple preconfigured flow models — such as banking or pharmaceutical workflows — using visual thumbnails, enabling teams to handle diverse datasets and domains within a single unified interface.
• Automatically detect upstream schema changes and alert downstream users: Track metadata changes in real time across pipelines, ETL jobs, and connected systems. Receive instant alerts when schema modifications, broken links, or logic changes occur before they cause downstream failures, and leverage built-in impact preview to speed up root cause analysis and reduce operational risk.
• Auto-map lineage with AI-powered metadata extraction: Use artificial intelligence to automatically discover and connect metadata across databases, ETL tools, BI platforms, and APIs. FlowGenX generates lineage graphs without manual input by learning transformation logic and usage patterns, eliminating documentation silos and scaling lineage coverage effortlessly across the entire data ecosystem.

FlowGenX is designed to serve enterprise environments with complex, multi-system data architectures. Its support for preconfigured domain-specific flow models, real-time change detection, and AI-driven automation makes it a scalable solution for organisations seeking to align data operations with regulatory standards while reducing the manual overhead traditionally associated with data lineage management.