The biopharma industry has fully embraced a data-centric approach. From genome sequencing and proteomics to Electronic Health Records (EHRs) and clinical trial data, researchers now handle petabytes of complex, multimodal datasets. But this data is only as valuable as the insights it can generate. In this landscape, the right analytics platform can make or break your R&D pipeline.

Across the globe, biopharma companies are accelerating their digital transformation journeys. In the United States, around 80% of biopharma leaders expressed the need to adopt AI and advanced analytics tools to improve drug discovery and development efficiency. Meanwhile, Europe is investing heavily in data harmonization and interoperability, with programs like Horizon Europe prioritizing cross-border data sharing and adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.^[1]

The global pharmaceutical analytics market is projected to grow from $15.27 billion in 2023 to $37.20 billion by 2030, driven by increasing R&D complexity and the need for precision therapies. But with dozens of vendors offering overlapping solutions – from point tools to end-to-end platforms – choosing the right one can be overwhelming.

Elucidata offers customizable data analytics solutions for the biopharma industry. Whether a mid-sized biotech company or a global pharma company, our platform has the solutions to cater to every biopharma need. In this blog, we provide a step-by-step guide to evaluate, compare, and select the best-fit data analytics platform to accelerate discovery.

Biopharma Data Landscape in 2025 - Too much fragmented data

Biopharma research today runs on data, which is not just of one kind. The diversity and complexity of datasets in this space make analytics uniquely challenging, but incredibly powerful, when done correctly. From raw sequencing files to real-world patient records, every piece of data carries potential insight, provided it is structured and connected the right way.

The types of data used in biopharma are multimodal and varied. Teams routinely work with structured and unstructured datasets, including genomics (FASTQ, BAM, VCF), transcriptomics, proteomics, and metabolomics files generated from next-generation sequencing and mass spectrometry platforms. Clinical trial data adds another layer, bringing in patient demographics, adverse events, efficacy outcomes, and longitudinal metrics often stored in CDISC-compliant formats like SDTM and ADaM. Then there are electronic health records (EHRs), often formatted in HL7 or FHIR standards, and real-world data such as insurance claims, wearables, patient-reported outcomes, and pharmacy interactions. Imaging data, ranging from radiology scans to pathology slides, requires high-volume storage and advanced pipelines for processing and analysis. Each of these data types has its own file formats, metadata structures, and integration demands, which become even more complex when you need to connect them across modalities.

The volume, variety, and velocity of data in biopharma are staggering. A single whole-genome sequence can generate over 200 GB of raw data, and a single clinical trial might track hundreds of endpoints across thousands of patients. According to the International Data Corporation (IDC) , by 2025, healthcare and life sciences will be responsible for more than 36% of all global data, largely driven by genomics, wearable devices, and EHR systems.

With this scale comes regulatory responsibility. Biopharma data is almost always patient-linked, making privacy, security, and compliance non-negotiable. In the U.S., HIPAA mandates strict controls for managing identifiable health information. In Europe, GDPR enforces stringent consent protocols and limits on cross-border data transfers. The FDA’s 21 CFR Part 11 governs electronic records and signatures in clinical trials. Beyond these regulatory baselines, many organizations, especially in Europe, are aligning with FAIR data principles to ensure data is not only secure but also findable, accessible, interoperable, and reusable across teams and tools.

Still, the current landscape has a considerable amount of friction. Data silos persist between departments and across development stages. Systems often lack compatibility, making integration slow and error-prone. Standardization is inconsistent, especially when older legacy systems must work alongside new ones. As data volume increases, scalability becomes a concern. Many platforms fall short of supporting the flexible, domain-specific analytics workflows scientists actually need.

Understanding these challenges is a critical first step before selecting a data analytics platform. The right solution must be capable of handling diverse, high-volume datasets, be flexible enough to grow with evolving scientific workflows, and be built to support global compliance from the ground up.

Choosing the Right Platform

Developing a single drug can cost over $2 billion and take more than 10 years.^[2] A wrong decision can delay therapies, derail research, trigger regulatory setbacks, or increase financial burden. Data analytics platforms play a central role in shortening this cycle by enabling better decision-making across drug development.

At Elucidata, we have seen how choosing the right platform can influence R&D outcomes. Polly, our data-centric platform, is designed to support every phase of drug development, from biomarker discovery through clinical trials, by making biomedical data analysis-ready, interoperable, and usable for scientists and data teams alike. Polly helps reduce time-to-insight and improves confidence in data-driven decisions.

A poorly configured or non-compliant system risks HIPAA or GDPR breaches, with costly fines and lasting damage to credibility. Polly builds security and compliance into workflows from the start, with enterprise-grade governance, role-based access control, and audit trails.

Disconnected tools and manual processing slow down research and introduce errors. Polly replaces this friction with a unified, intuitive interface that supports cross-functional collaboration. And because science is evolving fast, Polly is built to scale, with support for new data types, ML methods, and changing regulatory requirements.

With cost, time, reputation, and lives at stake, choosing the right analytics platform can have profound impacts. Elucidata helps teams make that decision easier, which has a domino effect on the speed of scientific insights.

Step 1: Align the Platform With Your Data and Goals

Before evaluating vendors, it’s important to define what you need the platform to do and how well it can integrate with your existing data ecosystem. Biopharma teams often have different priorities: some focus on accelerating target identification using multi-omics data, while others need to manage large volumes of clinical trial data or extract insights from real-world data and EHRs. Some might be building predictive models for drug response or collaborating across distributed teams. Polly is built to be flexible and usable across all these use cases, adapting to your pipeline, scale, and scientific questions.

Understanding how data moves through your team is equally important. From ingestion, i.e. pulling in data from LIMS, EHR, or EDC systems, to preprocessing, annotation, and transformation, Polly automates and streamlines every step. Once the data is structured, it becomes easier to apply statistical and ML methods for deeper insights and generate reports that are ready for regulatory, clinical, or research audiences. All of this happens in a unified platform that connects cross-functional teams without workflow fragmentation.

Integration matters just as much as capability. In an industry still grappling with fragmented systems and data silos, Polly offers out-of-the-box support for formats like FASTQ, HL7, CSV, and VCF, along with pre-built pipelines that eliminate the need for constant engineering intervention. Polly integrates smoothly with cloud providers like AWS, GCP, and Azure, and interfaces with major lab and clinical tools while tracking metadata and data provenance. By ensuring all datasets – structured and unstructured – are accessible, harmonized, and analysis-ready, Polly enables teams to get to the science faster.

Step 2: Ensure Compliance and Data Governance

Biopharma data is not only sensitive, but also heavily regulated. Whether you're working with clinical trial records, patient health information, or genomic datasets, the stakes are high. Mishandling any of it can lead to legal consequences, regulatory delays, or reputational damage. Hence, compliance and governance must be foundational to any data management system.

Polly is designed with these realities in mind. It meets the requirements of HIPAA, GDPR, and 21 CFR Part 11 by embedding controls directly into the platform. From role-based access to encryption at rest and in transit, audit trails, consent tracking, and data masking, Polly enables compliance without compromising usability.

It also supports data residency controls, which are critical when managing multi-region trials, so that teams can control where data is stored and processed. For cross-border collaborations, Polly is equipped to handle the complexities of regulations like Schrems II, ensuring peace of mind while maintaining productivity.

Good governance reduces risks while enabling collaboration. For example, with Polly, data is trackable, versioned, and clearly annotated, reducing ambiguity and helping scientists trust the data they’re using. For biopharma teams operating across departments, partners, and countries, that clarity is essential.

When compliance is built into the foundation, as it is with Polly, teams are free to focus on the science, and need not worry about managing risk retroactively.

Step 3: Build for Insight, Not Just Storage

A data platform should do more than store information, it should also help you make sense of it. In biopharma, that means enabling researchers and data scientists to extract insights quickly, test hypotheses, and apply advanced analytics or machine learning models to complex biological problems.

Polly is designed to be as good of a data analytics platform, as it is of data storage and management. It supports a wide range of analytical workflows, from standard statistical analyses to exploratory data science and predictive modeling. Users can launch pre-built workflows or bring their own models using Jupyter notebooks, R, or Python. Polly also supports batch processing, version control, and reproducibility by default, which are essential for both exploratory and regulated environments.

For teams working on biomarker discovery, for example, Polly enables them to normalize and filter expression data, apply ML models, and explore output through dynamic, interactive visualizations, all within the same platform. The result is a faster path from data to decision, with less friction and more transparency.

As the role of AI in biopharma continues to expand, platforms like Polly ensure you’re not just collecting data, but putting it to work.

Step 4: Choose a Partner Who Knows the Science

Not all vendors are created equal. Choosing a platform provider that understands the complexity of biomedical research makes a difference, not only in the quality of the product, but in the level of support and alignment you can expect.

Polly was built specifically for life sciences and biopharma. This means that we provide native support for biomedical file types and ontologies, use-case-specific workflows, and features designed with R&D teams in mind. Our team collaborates with customers to enable complex analyses. We speak the language of the scientists who use our platform, and we customize downstream applications with their needs in mind.

Whether you're working on early-stage discovery, clinical development, or translational science, Polly is grounded in the workflows, data types, and constraints you face.

Step 5: Scale Without Rebuilding

Drug development is not static, and therefore data infrastructure shouldn’t be static either. What works for one team or project may quickly become limiting as your research grows in size, complexity, or geographic footprint.

Polly is cloud-native and built to scale, whether you're adding users, working across regions, or handling terabytes of high-dimensional data. It supports elastic compute, parallel processing, and integration with HPC resources where needed. It also enables granular governance and access control, so scale doesn’t come at the cost of compliance.

More importantly, Polly allows you to start small and expand over time without needing to rebuild workflows or migrate data. This makes it ideal to deploy Polly for fast-growing biotechs, mid-size pharmaceutical companies, and research organizations looking to modernize without disruption.

Conclusion

In biopharma research, where data volume and complexity grow by the day, your analytics platform can be the competitive advantage. The right platform transforms raw data into reproducible insights, streamlines collaboration across R&D and clinical teams, and ensures regulatory compliance without sacrificing speed. The wrong one can slow everything down, leaving scientists stuck in spreadsheets and IT teams overwhelmed with integration workarounds.

At Elucidata, we customize data solutions for life sciences, enabling teams to unify, curate, and analyze multi-omics, assay, clinical, and real-world data, all on a single, FAIR-compliant platform. With native support for scientific metadata, seamless integrations with systems, and readiness for compliance with regulatory standards, our data solutions are designed to meet the unique needs of biomedical research. Our AI-ready infrastructure, scalable cloud-native architecture, and intuitive tools for both technical and non-technical users make it easier to move from data to discovery. Book a demo today to learn how we can help you not only manage data, but also help you extract meaning from it and bring therapies to the market faster.

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