Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: Pharmaceutical companies are faced with growing data complexity in clinical, regulatory, and commercial areas, with old centralized architectures unable to provide the agility and scalability required for current drug development and commercialization. An emerging architecture utilizing Data Mesh principles with Robotic Process Automation (RPA) and DataOps provides a framework to build intelligent data product pipelines that are domain focused and allow domain teams to take ownership of their data products while also applying automated processing to repeatable activities such as data ingestion, metadata registration, quality validation, lineage verification, and compliance checks. The proposed architecture establishes self-service infrastructure capabilities and federated governance models that embed automation at critical integration points, facilitating continuous integration and deployment of trusted data products. Real-world implementations demonstrate significant improvements in data product delivery cycles, quality metrics, and regulatory compliance across clinical trial standardization, omnichannel analytics, and regulatory reporting workflows. The model or framework enables pharmaceutical organizations to meaningfully scale their data ecosystems with less manual overhead while still maintaining the important governance and quality controls critical for healthcare innovation. This architectural convergence signifies a shift from large monolithic data platforms to decentralized, more automated ecosystems that allow for quicker insight generation and position pharmaceutical organisations for large-scale AI-enabled innovation.