Ontology-Driven Ecosystem for Biomanufacturing Data
Industry
Bio-pharmaceutical / Manufacturing
Scope
Data Lakehouse / Semantic Modeling / System Architecture / Validation
Timeframe
7 months
-
75%
saved time for OPV
report generation -
4
hours to deploy data
platform from scratch -
10
team members
01
CLIENT
A leading European bio-pharmaceutical manufacturer operating in a highly regulated production environment (GxP).
02
BUSINESS NEEDS
The primary objective was to establish a Unified Data Platform to serve as the "Single Source of Truth." The client needed to break down data silos between SCADA, LIMS, and paper records to replace manual compilation with near real-time analytics for Ongoing Process Verification (OPV).
03
CHALLENGE
To help our client achieve data unity, we overcame the following challenges:
-
Source Heterogeneity
Integrating vastly different data structures, from transactional SQL databases to high-density time-series data from OT sensors. -
Regulatory Compliance
Ensuring every data operation is fully auditable and validated according to strict GxP requirements. -
Restrictive Environment
Building high-performance infrastructure in isolated on-premise networks requiring precise offline dependency management. -
Validation Continuity
Creating an environment that ensures Data Integrity at every stage – from ingestion to final reporting.
04
SOLUTION
We implemented an agile Ontology-Driven Data Lakehouse architecture. This approach separates business logic from code, allowing the system to "understand" physical connections. The solution entailed:
- Semantic Process Model Using ontologies to map physical production parameters to universal business classes for rapid onboarding.
- Streaming Readiness A hybrid architecture designed for both batch processing and continuous real-time data ingestion.
- Modular Transforms Pre-validated processing components that automatically align source data with the target business model.
- Rapid Deployment Full containerization allowing the complete deployment of platform components onto infrastructure in just 4 hours.
In classic ETL, business logic is deeply embedded within the code. In our solution, the logic resides in the Ontology. This empowers non-technical Domain Experts to influence the data model without writing code. Unlike traditional approaches that provide only "raw joins," this system "understands" the physical connections within the biotechnological process. This delivers data in its full business context, creating the essential foundation for Advanced Analytics and Digital Twins.
Patryk Konarski
Data Platform Leader
Technology used
05
OUTCOME
The project delivered a validated data engine that transforms raw process information into business value. It enables a unique correlation between R&D parameters and mass production, serving as the foundation for the client's upcoming Digital Twin project.
- Efficiency Gains Reduced multi-week compilation processes for OPV reports to a fraction of the time, saving 75% of the effort.
- Eliminated Silos Successfully merged data from SCADA, LIMS, and ERP systems into a unified view of every production batch.
- On-Demand Scalability New production areas can be onboarded without rebuilding IT foundations thanks to reusable ontological models.
- Full Compliance The system ensures full auditability and data integrity, confirmed by a comprehensive GxP validation package.
06
IMPLEMENTED SOLUTION
-
Logic Separation Business rules reside in the Ontology, not the code, allowing experts to manage the model effortlessly.
-
Automated Lifecycle The system automatically recognizes relationships between objects (e.g., LIMS Samples vs SCADA Batches).
-
Advanced Analytics Moving beyond "raw joins" to deliver data in full business context, enabling future Digital Twin capabilities.
-
Hybrid Handling A unified model capable of handling both batch-oriented data and real-time streaming simultaneously.
-
75%
saved time for OPV
report generation -
4
hours to deploy data
platform from scratch -
10
team members