Insights

Global CDMO Builds a Modern Enterprise Data Lake with Beyondsoft 

Author: Rohit Garg

THE CHALLENGE

Our client, a global Contract Development and Manufacturing Organization (CDMO) with multiple subsidiaries and manufacturing sites, faced major challenges at their condition-based maintenance (CBM) location. Core enterprise applications, including Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES), operated independently of each other. This created data silos, duplicate reporting, and slow analytics turnaround. 

Leaders needed a modern solution to unify data, enforce governance, and provide a single source of truth that supported both batch and streaming ingestion. Without this, business and operational teams lacked timely visibility into critical key performance indicators such as: 

The absence of trusted metrics made it difficult to drive continuous improvement across manufacturing, supply chain, finance, and quality. Key pain points included inconsistent data across units, time-intensive Extract, Transform, Load (ETL) workflows, reporting bottlenecks, and a limited foundation to support advanced Machine Learning and AI initiatives. 

Our client set a clear goal: establish an Enterprise Data Lake (EDL) that would bring information from multiple systems into one place and give teams a single source of truth, make data easier to access, and improve both decision-making and compliance. 

THE SOLUTION

Our prior work together built trust and a proven track record, so the CDMO turned to us. We were entrusted with full ownership of the project, from discovery through implementation, supported by our client and their stakeholders.  

Our team knows our CDMO client’s enterprise data platforms inside and out, including Microsoft Azure, Microsoft Fabric, Databricks, Power BI, and other advanced data engineering tech stacks. But we also understand our client’s business: what drives ROI, how data decisions impact their bottom line, and where they need quick wins. From day one, we embedded governance by design with a clear approach to data cataloging, quality checks, role-based access control (RBAC), and compliance that matches what our client already has in place. We used proven accelerators, ran regular sprint demos, and quality checkpoints. We reduced risk and delivered results quickly without cutting corners.  

We partnered with our client to design and deliver a Microsoft Fabric–powered enterprise data lake on Azure. The platform consolidated data across ERP, MES, Quality Management System (QMS), Laboratory Information Management System (LIMS), Computerized Maintenance Management System (CMMS), and other applications at the CBM site. 

The project was executed in three phases: 

Phase 1: Foundation 

• Established a unified EDL on Microsoft Fabric 
• Automated ETL and data engineering processes with metadata-driven frameworks 
• Defined common data models to ensure consistent reporting

Phase 2: Reporting and Insights 

• Integrated Azure Fabric and Spark Job-based pipelines to enable faster queries 
• Developed advanced Business Intelligence dashboards tailored to different lines of business 
• Enabled KPI tracking for executives and site leaders

Phase 3: Future Readiness 

• Built a foundation for ML and AI use cases, such as predictive quality and yield optimization 
• Batch and API-based near real-time streaming for telemetry and time-sensitive data 
• Introduced Continuous Integration and Continuous Deployment processes to support scalability and governance

Our delivery approach emphasized collaboration and adoption. Agile sprints included demos and quality checkpoints, while train-the-trainer sessions empowered internal power users. User acceptance testing and hyper-care support ensured a smooth transition post go-live. 

THE RESULTS

Within the first deployment cycle, our CDMO client achieved measurable improvements at the CBM site: 

• Near real-time, single source of truth: Reduced reconciliation cycles and improved trust in KPIs. Executives can now monitor performance in real time. 
• Faster insights: Cut reporting time from days to hours. Delivered dashboards across finance, supply chain, quality, environmental health and safety, and environmental, social, and governance (ESG). 
• Stronger governance: Implemented role-based access, transparent data lineage, and compliance-ready controls. 
• Future-ready platform: Boosted productivity for data engineering teams with metadata-driven automation. Positioned for ML and AI innovation to enable predictive analytics and continuous improvement. 

By working with our CDMO client to build a Microsoft Fabric-powered data lake, we consolidated diverse systems into a single source of truth. This improved visibility, accelerated reporting, and prepared them for future growth in AI, ML, and advanced analytics.

Rohit Garg, Director, Account Management, Beyondsoft

NEXT STEPS

We continue to work with our client to scale the solution across additional sites and business domains. Future initiatives include expanding real-time integrations and accelerating high-priority AI and ML use cases, building on the established data lakehouse foundation. 

Interested in improving your operations? Contact us to discuss how a modern enterprise data lake can fix your workflows and speed up decision-making.