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Enterprise Clinical Data Modernization with DATAstream

How a Unified Clinical Data Hub Cut Manual Work by 80% and Delivered Near Real-Time Trial Visibility

Emerging Biotech
2 MIN READ

Challenge

Clinical data fragmentation across CROs, systems, and sources created operational bottlenecks. Clinical data originated from multiple vendors, EDC, RTSM, Imaging, Bioanalytical Labs, & Sample Management, each providing data in different formats, structures, and delivery frequencies. This led to:

  • Significant data latency due to manual aggregation from disparate systems
  • No standardized data model to support cross-trial analytics or scalability
  • Inconsistent validation and limited traceability, increasing downstream risk
  • Redundant data handling across teams, driving operational inefficiencies

Without an integrated foundation, the teams lacked real-time visibility into trial performance and site-level metrics.

Solution

Slipstream deployed DATAstreamTM Clinical to centralize, standardize, and automate ingestion, transformation, and delivery of all clinical datasets. The solution included:

  • Azure Data Factory (ADF) & Synapse Pipelines orchestrated through DATAstream’s metadata-driven framework
  • Azure Data Lake Gen2 for secure, scalable storage and archival
  • Synapse Data Warehouse structured using DATAstream’s Medallion architecture for analytics and governed access
  • Logic Apps for automated monitoring, alerts, and notifications
  • Power BI & Power Apps for role-based visualization and operational dashboards
  • DATAstream’s validation engine ensuring standardized ingestion and quality controls across all clinical sources
Slipstream’s DATAstream Clinical platform unified our end-to-end clinical data ecosystem, delivering real-time visibility, improved quality, and operational efficiency across all trials and teams.

Results

The customer cut manual aggregation by 80%, gained near real-time study visibility, improved decision-making with consistent data, strengthened cross-team collaboration, and reduced costs by eliminating redundant reconciliation and purchases.

Highlights

The customer cut manual aggregation by 80%, gained near real-time study visibility, improved decision-making with consistent data, strengthened cross-team collaboration, and reduced costs by eliminating redundant reconciliation and purchases.