Life‑Sciences Data Platforms

Life-sciences companies are rebuilding research systems before they scale artificial intelligence, starting with projects that pull scattered lab, clinical and regulatory data into one platform. (clarkstonconsulting.com) In a case study published April 22, 2026, Clarkston Consulting said it helped a client’s research and development group design a scalable data-science platform for genomics and computational biology work tied to AI-enabled drug discovery. The firm said the effort included interviews with scientists and directors and surfaced more than 25 challenges tied to silos, weak governance and limited collaboration across therapeutic areas. (clarkstonconsulting.com) Birlasoft published a separate case study describing work for a Fortune 10 global medical-device manufacturer whose research data was split across clinical trial systems, laboratory databases, device telemetry platforms and regulatory sources. Birlasoft said it built a cloud-native data lake to ingest and harmonize structured and unstructured data so researchers and regulatory teams could work from a single source of truth. (birlasoft.com) A data lake is a large storage layer that keeps many kinds of raw information in one place, like putting files from dozens of cabinets into one searchable warehouse. Clarkston said drug-discovery teams wanted to unify multi-modal data to identify novel targets, while Birlasoft said its client needed one platform that could support real-time analytics and artificial intelligence. (clarkstonconsulting.com) (birlasoft.com) The bottleneck is not a lack of data but the way life-sciences companies store it. Clarkston said research data often sits across electronic data capture systems, trial-management systems, electronic trial master files, SharePoint folders, Teams channels, email and paper forms, which makes reuse and cross-study analysis harder. (clarkstonconsulting.com) Regulation is part of the architecture decision. Birlasoft said its medical-device case had to account for Health Insurance Portability and Accountability Act, General Data Protection Regulation and Food and Drug Administration requirements, while Clarkston said data strategy in life sciences has to stay compliant from early clinical work through commercial launch. (birlasoft.com) (clarkstonconsulting.com) Both firms framed the platform work as a prerequisite for faster research and product decisions, not just an information-technology cleanup. Clarkston said advanced AI use cases in drug development require a strong data foundation, and Birlasoft said life-sciences modernization is being sold internally on speed, compliance and long-term operating value rather than infrastructure alone. (clarkstonconsulting.com) (birlasoft.com) The immediate result in both case studies is organizational as much as technical: new governance groups, shared standards and a common view of research data across teams that usually work in separate systems. That is the groundwork these companies are laying before they ask artificial intelligence to find a drug target or speed a device submission. (clarkstonconsulting.com) (birlasoft.com)