What Pharma Can Learn from Data Science Outside the Industry

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Clinical development is generating more data than ever before, yet many organizations still struggle to turn that data into timely, actionable insight. While other industries, such as manufacturing, logistics, and technology, have spent decades refining how they stage, validate, and operationalize data at scale, life sciences has often remained inward‑looking when it comes to data strategy.

At PHUSE, Kris Wenzel, Director, Data Science, Biometrics, explored how proven data science approaches from outside pharma can help clinical teams improve visibility, consistency, and decision‑making across the trial lifecycle.

Learning from Industries Built on Operational Data

In manufacturing and other data‑driven operational environments, data is treated as a critical asset. Systems are designed to ensure that data flows reliably between platforms, is validated continuously, and is made available in near real time to support day‑to‑day decisions. Factory floor dashboards do more than show historical performance; they surface risks, bottlenecks, and emerging issues early, enabling timely intervention.

By contrast, clinical trial data is often fragmented across systems and functions, limiting visibility until late in the process. Applying these data engineering practices, focused on structured data pipelines, continuous validation, and reliable data flow, can help ensure that clinical data is consistently structured, traceable, and ready for analysis as it is generated.

Delivering data faster and more reliably improves efficiency but also enables clinical teams to act earlier, reducing delays and supporting more confident, timely decisions throughout the study.

Applying Scalable Data Engineering Practices to Clinical Data Pipelines

In mature data environments, scalable pipelines are designed around automation, transparency, and repeatability. Automated processes reduce manual handoffs, enforce consistent standards, and surface issues earlier in the data lifecycle.

Applying these principles to clinical data pipelines allows teams to standardize data processing and quality checks across studies and functions. This reduces variability, improves data reliability, and ensures that data is consistently available when it is needed.

The impact is twofold: not only does this improve operational efficiency, but it also strengthens the ability to deliver high‑quality data quickly enough to support time‑sensitive decisions, particularly in early‑phase or adaptive trials where speed and accuracy are critical.

Solutions such as Datacise® are designed to support this model by enabling faster, more consistent data delivery across systems. By streamlining how data is prepared, validated, and made accessible, they help operationalize the kind of reliable data pipelines that underpin more effective decision‑making.

Real‑Time Visibility Enables Better Decisions

One of the most powerful lessons from other industries is the use of real‑time dashboards to guide operational decisions. In manufacturing, leaders rely on live performance metrics to manage risk and adjust processes quickly. Similar dashboards in clinical trials can provide ongoing visibility into study progress, data readiness, safety signals, and emerging risks.

Rather than waiting for static reports, clinical teams can use these insights to intervene earlier, prioritize resources, and keep programs aligned with development goals.

Aligning Global Teams Around Shared Data

Large manufacturing organizations have long faced the challenge of aligning globally distributed teams around a single source of truth. Their success depends on shared data standards, governance frameworks, and clearly defined ownership.

These same principles apply to multi‑site, global clinical trials. When data is standardized and accessible across functions, clinical operations, data management, statistics, and regulatory, teams can collaborate more effectively and maintain alignment throughout the study lifecycle.

Building a Stronger Data Foundation for Clinical Research

My presentation highlights how the pharmaceutical sector can progress by adopting proven approaches from other industries. By leveraging structured data pipelines, continuous validation, real‑time visibility, and collaborative governance, clinical teams can improve quality, reduce inefficiencies, and accelerate decision‑making.

As clinical trials grow in complexity, the ability to deliver reliable, decision‑ready data quickly will become increasingly important. Looking beyond pharma, and investing in scalable data engineering approaches, will be key to getting there.