A global pharmaceuticals giant was facing a data crisis. Years of rapid growth had left its data ecosystem in rough shape: Legacy warehouses isolated precious information while analytics teams struggled through a complex web of R scripts, many auto-generated by AI tools and lacking proper documentation. Knowledge that should have optimized drug development and supply chains was obscured by layers of custom code, slow reporting, and opaque processes. The result was a fragile, black-box system that limited development.
Recognizing these troubles, the company partnered with DI Squared to undertake a comprehensive modernization of its data engineering and analytics infrastructure. Our approach emphasized aligning technology modernization with process improvements and governance enhancements. The company needed to rebuild data platform from its foundations rather than rely on superficial fixes.
Key Initiatives in the Transformation from a Black Box to a Modern Data Operation
To address the complex challenges comprehensively, we took a three-prong approach to designing and executing the client's tailored solution: Technology adoption, process standardization, and governance reinforcement.
Adopting Talend Cloud for Data Integration
The company’s sprawling R script ecosystem was replaced by Talend Cloud’s low-code, declarative data integration platform. This change had a profound impact. Talend Cloud enabled data engineering teams to build data pipelines through visual workflows and reusable components rather than fragile custom code, which made it significantly easier to maintain. This low-code environment accelerated development cycles by providing a standardized framework for designing, executing, and monitoring data flows. It also made onboarding new engineers easier, and eliminated the black-box effect of poorly documented scripts.
Developing Reusable ETL/ELT Patterns
To accelerate the development of data pipelines while maintaining consistency and reducing errors, we created a library of reusable Data Integration templates within Talend Cloud. These templates utilized Extract Transform Load (ETL) as well as Extract Load Transform (ELT) patterns to minimize egress costs and achieve the best possible performance. These template-based workflows covered common data preparation tasks such as sales data cleansing, clinical data standardization, and harmonization of supply chain inputs. The reusable nature of these components reduced duplication of effort, enforced data quality standards, and simplified implementation of governance policies across the enterprise.
Such standardization also meant greater agility. Teams could assemble new data pipelines by combining existing patterns to meet emergent business requirements, such as integrating new data sources or prepping datasets for specific analytical models.
Migrating to Snowflake for Data Warehousing
To overcome the limitations of the aging on-premises data warehouse, the company adopted Snowflake’s cloud-native platform. Snowflake’s architecture decouples storage from compute resources, enabling elastic scaling that adapts dynamically to fluctuating workloads. It catalyzed the consolidation of diverse data sources into a single coherent repository. Those included clinical trial data, global sales information, manufacturing metrics, and supply chain logistics. Centralizing data allowed for unified governance and consistent data definitions.
The migration also enhanced performance. Complex queries that previously took hours to execute now ran in minutes or seconds. Snowflake’s support for semi-structured data extended the organization’s capability to integrate emerging data types such as JSON-formatted sensor outputs and patient-reported outcomes.
Implementing Qlik for Analytics and Visualization
We integrated Qlik as the analytics and visualization layer. Qlik’s intuitive dashboards democratized data access by enabling business users across the company to conduct self-service analyses without relying excessively on data engineering or IT teams.
Qlik’s augmented analytics capabilities, including natural language querying and AI-driven pattern recognition, empowered users to uncover trends and anomalies with exponentially more efficiency.
Strengthening Data Governance and Collaboration
Modernizing technology is incomplete without strong governance. We helped the company implement comprehensive governance frameworks, emphasizing automated data lineage tracking, version control, and workflow approvals. Talend Cloud and Snowflake facilitated transparent documentation of data origins, transformations, and consumption. Shared metadata repositories and training programs strengthened collaboration through, e.g., shared terminology.
Results
- Development cycles for data products dropped from weeks to days
- Teams gained visibility and control with a visual, low-code integration environment
- Cloud-native infrastructure improved scalability and reduced operational overhead
- Business units began generating value from data more rapidly, supporting R&D, compliance, and commercial analytics
Data Lessons to Live By: From Fragile Environments to Free-Flowing Insights
This transformation journey shifted a global pharmaceuticals company from a fragile and siloed data environment into a highflyer. Thoughtful modernization can convert data from a delay into a foundation for real strategy, vision, and leadership.
Several overarching lessons emerged from this endeavor that can guide similar modernization projects, irrespective of industry. Think of these as ‘guidelines’ not ‘step-by-step' instructions.
- Prioritize maintainability through low-code, visual data integration tools.
- Leverage cloud-native architectures like Snowflake to support diverse data types.
- Foster collaboration through shared metadata, automated lineage, and governance frameworks.
- Support cultural change via training and communication to maximize the value of technical investments.
How DI Squared Helps
Join the “Wall of Healthcare Data Heroes”. Reach out to DI Squared today for a free 1:1 chat about your obstacles and goals for data analytics and engineering at your company.
