Interoperable Data Orchestration Services for Clinical Development and Research

The Challenge

A global biopharma working across development, research and commercial functions was growing its data landscape with new systems, expanding scientific activities, and increasing external data sources. The organisation wanted its data ecosystem to operate in an interoperable manner, enabling consistent processing, reliable movement and smoother preparation for analytics and ML initiatives.

Key Challenges Included:

Heterogeneous Data Across Multiple Functions
Clinical trial platforms, laboratory devices, research tools, ERP CRM systems and commercial applications each produced information in different structures. The customer wanted these datasets to move into their environment through a predictable and interoperable approach.
Department Level Pipelines and Scripts
Teams managed separate mechanisms for their data movement. The organisation sought a simplified and repeatable pattern for consistent data processing.
Faster Access to Analytics Ready Data
Analysts, researchers and business users required data to be prepared through a structured path, supporting analytics and ML development.
Governed Data Handling Requirements
As data sources expanded, the organisation placed emphasis on lineage, masking and metadata oversight to align with regulatory expectations.

Our Solution

We enabled the customer with an interoperable data orchestration approach that standardised how datasets flowed from source systems into their datalake environment. This service offered a scalable and governed method for preparing clinical, research and commercial data for downstream platforms.

Interoperable Pipelines Across the Organisation

Reusable and automated pipelines securely moved data from varied internal and external systems into enterprise storage, reducing manual interventions and ensuring consistency.

Structured Layered Data Processing

Incoming datasets followed recognised stages commonly used in datalake architectures, allowing analytics teams to work with well-prepared information.

Centrally Orchestrated Workflows

A neutral orchestration layer handled scheduling, monitoring and operational visibility, offering a clear view of pipeline performance.

Governance Embedded Throughout

Lineage, masking rules and metadata tracking were embedded into the integration process, maintaining compliance across global standards.

‍Platform Ownership Provided by Our Team

‍Our team managed the integration and orchestration environments on behalf of the customer, enabling a reliable and adaptable operational model.

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Key Impact

Up to 50% Faster insights

Data became analytics ready more quickly, supporting rapid analysis and model development.

Up to 30% Reduced cost

A governed orchestration approach replaced fragmented scripts and non-standard pipelines, decreasing operational overhead.

The Customer

The project was delivered for a global biopharma active in clinical development and research. Stakeholders included data engineering teams, analytics groups, research functions and enterprise platform owners who relied on interoperable data movement to support operational and scientific work.

The Outcomes

Interoperable Data. Consistent Decisions.

The interoperable data orchestration approach created a repeatable way for the customer to move clinical, research and commercial data into layers suitable for analytics and ML. Teams benefited from improved access to well organised data, while platform owners could oversee operations more effectively.