Persistent engineered a trusted research data foundation to help clinical and scientific teams at a leading biotechnology company access consistent data, collaborate more effectively and deliver scientific data products faster. Built on the Databricks Data Intelligence Platform, the foundation connects the client’s growing research data estate to decisions that support research and development (R&D).
Disconnected Data Held Research Back
For a biotechnology firm developing life-transforming medicines, data is not a by-product of research. It is the connective tissue between scientific questions, clinical insight and decisions that move therapies forward. Yet as the firm’s research ambitions and data volumes grew, that connective tissue was starting to crack.
With the cost per successful new molecular entity almost doubling from $2.5 billion in 2016 to $4 billion and clinical trial timelines growing by more than a third over the preceding decade, biotechnology firms need to make existing evidence easier to connect and use.
For our client, clinical, research and operational data flowed in from a wide range of internal and external sources, each with different formats and structures. Scientists, clinicians and data teams worked across disconnected datasets, limiting collaboration and slowing the movement of trusted insight across R&D.
More than any other reporting environment, this needed a data foundation that could scale with growing workloads, improve performance for demanding analytics and support the next generation of AI, machine learning and scientific data products.
Engineering Connective Tissue of Research Data Estate
Persistent partnered with the client to architect and implement a centralized Research Data Lake on the Databricks Data Intelligence Platform. The team connected clinical, research and operational data from internal and external sources, establishing the data flows and shared structures needed to bring the fragmented estate together as a synergistic system supporting scientific research.
To connect the estate, Persistent built more than 350 automated data pipelines to ingest data from 36 different sources and 12 domains, totalling up to 1,345 entities, into the platform. The team implemented cloud-native data engineering and analytics frameworks to handle growing research workloads and provide the processing foundation for scientific data products and analytics.
Above the Databricks platform, Persistent established a shared semantic layer through consistent data structures, common definitions and governance across clinical and research data. This rebuilt the connective tissue: a common way to organize and interpret data across functions, linking source data to the scientific questions, analytics and research processes it needed to support.
Persistent leveraged Unity Catalog’s reporting and monitoring capabilities for visibility into data, platform health and usage, alongside governance controls for a regulated life sciences environment. Together, these components ensured the research data foundation was secure, governed and grounded in enterprise truth, with an architecture designed to accommodate future AI and emerging scientific use cases.
From Fragmented Data to Shared Discovery Engine
With more than 350 automated data pipelines in place, research data now flows reliably and repeatably from diverse sources into the platform, reducing manual effort and making information available more consistently to downstream users. Easier access and sharing help researchers, clinicians and data scientists collaborate more effectively and move insight across R&D with less friction. This triggers network effect: the same governed data can support questions across functions and insight developed by one team becomes input for another. As reuse expands, the foundation has the potential to generate value through more connections between teams, datasets and decisions, not simply through greater data volume.
Business-specific scientific data products reach stakeholders faster, supporting more timely decisions. Improved scalability, performance and operational efficiency allow the platform to keep pace with expanding research workloads. And with an AI-ready foundation in place, the company is positioned to turn its growing body of research data into a durable advantage across the drug development lifecycle.
With better collaboration, faster delivery of scientific data products and a platform that can scale with research demand, the client has tapped into a longer-term opportunity of compounding reuse: each additional use of trusted data can extend its value beyond the team or question it originally served. That is the promise of stronger connective tissue—scientific insight that can travel further across the organization and inform more of the decisions that move development forward.
