Large Clinical Trial Company — Optimizing Cancer Research through MySQL Data Warehouse and AWS QuickSight Analytics
Client Overview
A large clinical trial company focused on cancer research and patient enrollment manages thousands of participants across multiple ongoing studies. The company partners with research institutions and pharmaceutical sponsors to streamline recruitment, improve operational efficiency, and enhance trial outcomes.
As data volumes increased across various systems and study sites, the organization sought to unify its data sources and leverage analytics to optimize current and future trials.
Problem: Disconnected Data and Manual Analytics
The organization’s operations generated large volumes of transactional data across patient enrollment systems, investigator reports, and site management tools.
Despite this wealth of data, insights were difficult to obtain due to siloed systems and manual aggregation processes.
Challenges Faced:
– Fragmented data stored in multiple databases and Excel reports.
– Manual extraction and transformation slowing down reporting cycles.
– Inconsistent data formats across trial sites and study phases.
– Delayed insights into recruitment and trial progress.
– Limited ability to generate predictive analytics for future optimization.
To address these challenges, the company needed a centralized analytics framework built directly on its MySQL transactional data, capable of producing real-time insights through interactive dashboards.
Solution: MySQL-Centric Analytics and AWS QuickSight Dashboards
Business Compass LLC partnered with the clinical trial company to design and deploy a modern data analytics architecture powered by MySQL and Amazon QuickSight.
The solution standardized data processing, built analytical models for trial insights, and enabled real-time visualization of key metrics across the organization.
1. Data Integration and Modeling
– Built the analytics layer directly on MySQL, serving as the central source of truth for patient and trial transactional data.
– Designed star-schema data models to organize information on patients, sites, trials, and sponsors efficiently.
– Implemented automated SQL scripts to aggregate and transform data from multiple tables.
– Created derived views and summary tables for fast analytical querying.
2. Data Transformation and Governance
– Automated nightly refreshes to keep analytics up to date.
– Implemented validation rules for data quality and compliance.
– Maintained audit-ready data lineage for transparency and regulatory adherence.
3. Advanced Analytics and Visualization
– Developed Amazon QuickSight dashboards connected directly to MySQL for live, interactive visualization.
– Dashboards included:
– Patient Enrollment Tracker – recruitment progress by site and phase.
– Site Performance Overview – site efficiency and enrollment trends.
– Trial Progress Monitor – completion metrics and participant demographics.
– Enabled drill-down analytics for operational and clinical teams.
4. Insight-Driven Optimization
– Empowered leadership to identify recruitment bottlenecks and underperforming sites.
– Provided data-backed recommendations for optimizing trial design and resource allocation.
– Enabled forecasting and predictive analysis for upcoming studies.
Outcome: Data-Driven Transformation of Clinical Trial Management
The MySQL and QuickSight integration transformed how the organization monitors and manages clinical trial operations.
Before vs After:
– Fragmented systems and manual reports → Unified analytics layer in MySQL
– Delayed reporting → Real-time dashboards powered by QuickSight
– Inconsistent data definitions → Standardized schema and validated transformations
– Reactive analysis → Automated, proactive insights
– Limited visibility → Comprehensive transparency across sites and studies
Key Results:
– 80% reduction in manual data processing time.
– Real-time access to patient and trial performance metrics.
– Improved operational decision-making and collaboration.
– Enhanced efficiency and predictive planning for future trials.
Technology Stack
– MySQL – Primary data source and analytical foundation
– Amazon QuickSight – Visualization and interactive dashboards
– AWS Lambda – Data refresh automation
– CSV / Excel Imports – Integration of historical and external datasets
Conclusion
Through its collaboration with Business Compass LLC, the large clinical trial company successfully implemented a scalable analytics framework using MySQL as its core data source.
By connecting MySQL directly with AWS QuickSight, the organization achieved real-time visibility into patient recruitment, trial progress, and site performance. This transformation enabled evidence-based decision-making, improved resource allocation, and optimized trial design strategies.
The initiative positioned the company as a leader in data-driven clinical research, empowering its teams to accelerate cancer trials and improve outcomes through actionable insights and advanced analytics.