Databricks in Pharma: How to Accelerate Drug Research and Development

Today, the average pharmaceutical company generates more data than ever before. Genomic data, clinical trial results, sensors installed on production lines, data generated during routine care (real-world data, RWD) and the evidence derived from it (real-world evidence, RWE), and HCP interaction logs – they’re all out there somewhere. The question is whether anyone is capable of seeing through all of this at once.

In most cases, the answer is no. Not because the technology doesn’t exist, but because research, clinical, manufacturing, and commercial data exist in different systems, follow different logics, and are managed by different organizations.

This is the real data problem in the pharma sector.

Let’s take a closer look at this – including how a flexible data platform like Databricks can help eliminate these issues.

Data Challenges and Solutions

1. The Silo Problem

Most pharma data architectures are built along functional lines: R&D has its own systems, clinical operations has its own, and manufacturing, quality and commercial each have theirs. These systems often work well in isolation. Together, they create blind spots.

The practical consequence: if a medical affairs team wants to run a real-world evidence analysis combining clinical, claims and EHR data, that’s typically a months-long project – not a weeks-long analysis. The promise of AI feels distant when basic data integration is still manual work. With a unified yet flexible data platform like Databricks, this task takes significantly less time.

2. The Governance Paradox

Pharma is heavily regulated: GxP, 21 CFR Part 11, auditability, reproducibility. In many organizations, this leads to conservative data management – and innovative analytics, ML, or AI use cases don’t get approved because governance at the data platform level hasn’t been resolved.

The Databricks lakehouse architecture addresses this directly: research, clinical, manufacturing and commercial data are unified on a single logical layer with consistent governance and a full audit trail. This doesn’t mean everything needs to live in one place physically – it means everything can be controlled under a single governance framework. Compliance and innovation no longer work against each other; they build from the same foundation.

3. The AI-Ready Foundation

Meaningful AI use cases in pharma – drug target identification, clinical patient segmentation, predictive manufacturing quality, HCP engagement optimization – depend on the same prerequisite: a unified, well-governed data layer on which models can be built and from which results can be audited. If this does not exist, AI cannot function effectively or reliably either.

The Databricks Data Intelligence Platform provides this foundation across the pharma and life sciences sector: spanning the full drug lifecycle, from discovery through manufacturing to the commercial organization. The company that builds this foundation now will reach working AI use cases 2–3 years faster than the one that starts with the model.

Databricks in Pharma: Typical Use Cases 

Optimizing Clinical Trials 

Clinical trials are complex and costly, and recruiting and retaining participants remains a persistent bottleneck. By leveraging patient demographics, historical trial data, eligibility criteria, electronic health records (EHRs), and insurance claims, organizations can use Databricks to

• optimize the selection of trial sites
• identify suitable participants across different populations
• forecast recruitment schedules and identify risks early on

Integration of RWE Data

The fragmentation of healthcare data (electronic health records, registries, claims) makes it difficult to analyze. Databricks can help consolidate this data into standardized formats (such as OMOP) that comply with regulations, in order to assess drug efficacy, treatment adherence, and patient risk.

Analysis of New Drug Launches

Databricks can also help analyze product launches by processing pharmacy and central data to track key performance indicators (NBRx, TRx), identifying barriers to access by payers (public or private health insurers), and determining the priorities of healthcare professionals during the launch of new drugs.

Supporting the Supply Chain and Manufacturing

Databricks supports the integration of global manufacturing systems, enabling predictive maintenance, the approval of exceptional batches, and real-time process optimization.

Accelerating Drug Research and Development

Databricks can help reduce the considerable amount of time currently spent on drug research and development – as demonstrated in the case study below.

Case Study: Using Databricks at AstraZeneca

 It is well known that the drug development process can take as long as 10–15 years, while less than 5% of new drugs make it to market, at significant cost. AstraZeneca sought to improve this situation with the help of Databricks.

This case study clearly illustrates that one of the greatest challenges in modern drug research and development is no longer a lack of relevant data, but rather its sheer volume and fragmentation. The company’s researchers have access to vast amounts of data from various sources – internal systems, scientific publications, and public databases – but integrating and quickly processing this data posed a significant obstacle to decision-making.

Among the key challenges, therefore, were managing a fragmented data environment, analyzing millions of data points from hundreds of sources, and ensuring the scalability and sustainability of the infrastructure. Traditional data processing approaches and open-source tools were unable to effectively support the growing volume of data and the demands of advanced data science models, preventing researchers from making informed decisions quickly enough.

AstraZeneca addressed this issue by implementing the Databricks platform, which provides a unified, scalable environment for integrating and processing data and running machine learning models. Using the platform, the company built a so-called knowledge graph that organizes biological relationships and serves as the foundation for a recommendation system. This recommendation system is capable of generating hypotheses regarding new drug targets while analyzing vast amounts of scientific literature using natural language processing (NLP). In addition, Databricks’ automated cluster management and scaling have significantly simplified infrastructure operations and made data and ML processes more efficient.

As a result of the implementation, AstraZeneca has realized significant business and operational benefits. The system processes millions of data points from thousands of sources, resulting in an order-of-magnitude increase in performance. Operational efficiency and the productivity of data science teams have improved, while researchers are able to arrive at actionable insights more quickly. The most significant outcome is the reduction in “time-to-insight” and “time-to-market”: the identification and validation of new drug targets have accelerated, which directly contributes to the company’s ability to develop new therapies more quickly and cost-effectively.

Overall, the case of AstraZeneca demonstrates that the integration of advanced data infrastructure and artificial intelligence provides a strategic advantage in the pharmaceutical industry. Organizations that manage their data on a unified platform and quickly derive business value from it can gain a significant competitive advantage in terms of R&D efficiency and the speed of innovation.

Closing Thought

In pharma, the data platform is no longer an IT initiative. R&D productivity, the speed of clinical development, manufacturing efficiency and commercial decision quality all stand or fall on the same data foundation.

As a Databricks partner, DSS Consulting helps pharma organizations navigate this path.  If you also think this data platform could be useful for your organization, it would be worth a discussion.