Life Sciences and Biotech Data to Intelligence Assessment on AWS

Life sciences organizations generate massive volumes of data across clinical trials, research, genomics, and real-world evidence. However, much of this data remains underutilized due to fragmentation across systems, lack of governance, and limited analytics maturity. Disconnected data environments, inconsistent data quality, and limited access to trusted datasets slow down research timelines, delay decision-making, and increase operational and regulatory risk. Compass UOL helps life sciences organizations assess and modernize their data landscape on AWS by transforming siloed data into a scalable, governed, and analytics-ready foundation. This assessment evaluates data sources, pipelines, governance models, and analytics capabilities to identify gaps and define a roadmap to move from raw data to actionable intelligence. Leveraging AWS data and analytics services, including data lakes, governance frameworks, and AI/ML integration points, Compass UOL defines an AWS-native architecture that enables faster insights generation, improved data accessibility, and standardized governance aligned with regulatory requirements. Customers leave with a clear path to unify their data environment, accelerate analytics initiatives, and enable data-driven decision-making across clinical, research, and operational workflows.

Buyer Problem / Business Trigger

Data silos across clinical, research, and operational systems Limited ability to generate insights from structured and unstructured datasets Poor data governance impacting quality, traceability, and compliance Delays in clinical and research decision-making due to lack of analytics readiness

Delivery Model

Discovery of data landscape and key business priorities Assessment of data architecture, pipelines, and analytics capabilities Definition of AWS-native data and intelligence architecture Roadmap creation for modernization and analytics enablement

Assessment / Engagement Scope

Inventory and evaluation of data sources (clinical trials, RWD, genomics, lab data) Assessment of data ingestion, storage, and processing pipelines Review of data governance, quality, lineage, and compliance controls Evaluation of analytics and reporting capabilities Identification of opportunities for AI/ML and advanced analytics integration Design of AWS-native architecture (data lake, analytics, governance layers)

Expected Output / Deliverables

Data maturity and intelligence assessment report AWS reference architecture for data and analytics in life sciences Data governance and quality framework Prioritized use cases for analytics and AI-driven insights Implementation roadmap for data modernization

Customer Decision Questions This offer helps the customer answer:

How do we unify clinical and research data into a single, trusted environment? Which AWS architecture enables scalable and compliant data analytics? Where can we accelerate insights generation to improve decision-making?