We got one shot at building a new data platform in a regulated context.

Life sciences organizations operate in a highly regulated and data-intensive environment, where the ability to quickly access and analyze critical information can be a game-changer. However, many companies struggle with siloed data, inefficient manual processes, and compliance complexities—especially when working with platforms like Veeva Vault, which is optimized for business process management but lacks robust analytics capabilities.

One biopharmaceutical company faced this exact challenge. Experiencing rapid growth, it needed to establish a scalable, high-performance data platform that could integrate Veeva Vault with Snowflake, enabling self-service analytics, automation, and compliance at scale. The challenge was clear: How do you transform a fragmented data landscape into a modern analytics ecosystem?

The Approach: A Structured Data Platform for Scalable Analytics

To solve this, the company implemented a systematic, phased approach to data platform development, focusing on:

Standardized Data Integration

• Automated data pipelines were developed to seamlessly ingest Veeva Vault data into Snowflake using Amazon S3 as a staging layer. The design and approach were considered together with GxP advisory.
• EL (Extract-and-Load) workflows were deployed using an internal accelerator at Tropos, “Data Loading Framework”. It’s a capability deployed through dbt that allows data, regardless of it’s metadata, to be loaded in Snowflake without the risk of interruption caused by schema changes.
• dbt (Data Build Tool) was adopted to manage transformations, ensure data consistency, and streamline testing. We went ahead with dbt given it’s a fairly transparent layer on top of Snowflake, allowing us to use the platforms capabilities without further interpretations by data transformations suppliers.
• A Data Vault modeling approach was used to create a flexible, scalable data architecture that adapts to evolving business needs. Data Vault is an excellent modelling technique for Snowflake, and dbt has excellent add-on modules to generate and maintain them. Our own accelerator “Tropos ReVault” ensures consistent management of the ever-expanding data model. Dbt ensures technical consistency and visual overview of the model.
  
  

Governance & Compliance by Design

The platform is strategic to the organization. Therefore, we focused on technical debt avoidance from the start, knowing future use cases would need to adhere to strict regulatory requirements.  The approach started with non-GxP workloads to build experience before expanding into regulated environments.

Snowflake, as a platform, typically fluidly meets the  Installation Qualification (IQ) requirements used in GxP. 

To meet the Operational Qualification (OQ) and Performance Qualification (PQ) requirements, a structured change management framework was developed, largely based on our dbt kickstarter accelerator.

By using this as a base, robust access controls, audit trails, and validation processes were implemented to meet the autit requirements.

And lastly, encryption and data masking techniques were applied to ensure sensitive data security while allowing authorized teams to conduct analyses without compromising compliance.

Self-Service Analytics Enablement

At the pace of growth, there’s hardly any time to communicate dashboard requirements. Therefore, we’ve co-created an operating model enabling self-service analytics from the start. 

Our anchoring points were

• A structured, business-centric data model was designed to empower end-users across different teams.
• BI tools such as PowerBI and Tableau were integrated with Snowflake to provide dashboards and ad-hoc reporting capabilities.
• Collaboration between IT, business, and regulatory teams ensured that the platform was both functional and compliant.

The Solution: A Future-Proof Analytics Stack

Technical Implementation

Our platform of choice was Snowflake. Since our full focus needed to go into solving business cases fast and reliable, the effort on maintaining technical infrastructure was largely outsourced to the SAAS vendor. Given it's breadth of capabilities, all of the processing happens inside the platform - thus within the contract perimeter between our customer and the software supplier.

Having such degree of freedom, most technical functions were fulfilled within the Data Cloud itself

• Data Integration: Daily automated data ingestion from Veeva Vault to Snowflake via Amazon S3, reducing manual work and improving data reliability.
  
• Data Processing: AWS Lambda was used to trigger data extraction jobs, while Step Functions coordinated the processing flow to handle retry mechanisms and logging.
  
• Data Warehouse: A centralized Snowflake repository with built-in compliance mechanisms and scalable storage. Knowing that Veeva Vault and several other data sources were to be interlinked for analysis, Data Vault as a technique shines here
  

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• Transformation & Processing: dbt-driven transformations enabled structured and version-controlled analytics-ready datasets.
  
• Metadata Management: A custom metadata layer was built within Snowflake to track data lineage, update frequency, and audit trails.
  
• Advanced Analytics: AI and machine learning workflows were integrated to provide predictive insights for key processes, using Snowpark for Python within Snowflake. 

During the development of the platform, enabling even faster self-service analytics for adhoc cases became available in the market. Cortex Analyst, a Snowflake capability that allows end-users to chat with their data and request statistics off structured data, was considered early after it’s general availability. 

Tropos’ seamless bridge between dbt and Snowflake’s Cortex Analyst delivered the capabilities, almost as a seamless bolt-on on top of the ongoing platform development. 

The Results: Measurable Business Impact

40% Faster Decision-Making and 30% Reduction in Manual Work: Time-to-insight was significantly reduced by streamlining data ingestion and analytics. Before the platform development, this was the average time an FTE would spend on gathering and standardizing reporting data manually and through the use of PowerBI as a personal data platform.

Compliance requirements validated: Structured governance mechanisms ensured alignment with regulatory standards.

Scalability for Growth: A modular architecture enabled seamless onboarding of new projects, use cases and business domains.

A key stakeholder noted:
“This data platform has transformed how we leverage our data for innovation, efficiency, and compliance.”

Key Lessons & Future Opportunities

The platform marks a milestone in the organisations’ analytical capability development. Many 

Collaboration is Critical: Success in regulated industries requires close cooperation between IT, business, and compliance teams.
Data Vault is a Game-Changer: This methodology enables adaptability and scalability as business needs evolve.
Governance Pays Off Early On: Investing in validation and compliance upfront simplifies scaling and future-proofing.

Next Steps:
Looking ahead, the company is exploring real-time data integration using Veeva’s Direct Data API, predictive analytics dashboards, and AI-driven automation to enhance operational efficiency further.

How Tropos Can Help

At Tropos, we specialize in building scalable, compliant data platforms for life sciences, leveraging Snowflake, dbt, and Veeva Vault to drive business transformation.

Need to integrate Veeva Vault with Snowflake? Let’s talk. Our structured approach accelerates analytics, ensures compliance, and unlocks new efficiencies. Contact us today.