How a global life sciences company modernized sales and marketing through a data-as-a-product approach

Summary

A leading biotechnology company partnered with Marlabs to modernize its sales and marketing data ecosystem through a “data as a product” (DaaP) approach. By overhauling their legacy data mart and migrating to a scalable Snowflake platform, the organization improved data governance, eliminated thousands of manual hours, and established a foundation for decentralized data ownership.

Challenge

The client faced significant hurdles in adapting their legacy sales data mart to meet the growing needs of a modern, decentralized business environment. The existing platform was rigid, lacked scalability, and did not align with the organization's evolving data strategy. As a result, it limited the company’s ability to efficiently manage North American sales data and leverage insights for decision-making. A modern solution was required to enable agility, improve accessibility, and support the broader data mesh vision.

Solution

To address these issues, Marlabs implemented a multi-phased solution to modernize the client’s data infrastructure. The initiative not only facilitated a technical migration to Snowflake but also embedded principles of data governance and decentralized ownership throughout the process.

Discovery

The engagement began with a thorough discovery phase aimed at understanding the current state of the client’s data architecture. Marlabs assessed existing systems, dependencies, and gaps that hindered operational efficiency. Key activities included stakeholder workshops to gather insights from various business units, documentation of existing dependencies, and a comprehensive analysis of the current state. This groundwork ensured the project was aligned with both technical constraints and business objectives from the outset.

Planning

Based on the insights from discovery, the team created a detailed migration strategy tailored to the client’s goals. This phase involved designing the future-state technical architecture and selecting the most appropriate platform—ultimately Snowflake—for long-term scalability. In addition to creating a migration roadmap, the team identified use cases that would benefit from enhanced capabilities in the modernized environment. This strategic planning phase was crucial in aligning technical execution with business priorities and securing stakeholder buy-in.

Implementation

With the roadmap in place, Marlabs proceeded with the execution of the migration. The team set up data ingestion pipelines, configured the Snowflake environment, and rolled out new data products aligned with the client’s operational needs. Rigorous testing ensured platform stability, while targeted user training facilitated adoption across teams. By the end of this phase, the new data platform was live, accessible, and already delivering value through streamlined operations.

Governance and optimization

In the final phase, Marlabs focused on embedding governance frameworks and optimizing the platform for long-term use. Governance workflows were established to manage data products efficiently, and Monte Carlo was integrated to enhance data quality and observability. The team also introduced processes for continuous improvement and performance monitoring, ensuring that the platform would remain adaptive to changing business needs. This phase solidified the transformation, empowering the client to sustain and evolve their data strategy independently.

Results

The project culminated in the successful migration of the client’s sales data mart to Snowflake. This transformation aligned perfectly with the client’s data mesh strategy and advanced their vision of a federated data culture. The modernized platform facilitated improved data ownership, boosted accessibility, and strengthened data-driven decision-making across business units. The client also experienced significant operational efficiencies, eliminating thousands of manual hours previously spent on data processing and reporting.

Impact

• Reduced reliance on manual processes for data movement, correction, and reporting
• Enabled decentralized data ownership across business units
• Improved data accessibility and usability for decision-makers
• Enhanced data quality through observability and governance tools
• Supported long-term adoption of a federated data culture through scalable infrastructure and strategic alignment