Life Sciences Analytics

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Accelerate digital transformation with data-driven engineering and predictive analytics

The top challenges in the life sciences industry include a lack of high-quality data sources, difficulties in integrating diverse data, and the absence of cross-functional analytics teams. At Sigmoid, we specialize in delivering data engineering and advanced analytics solutions tailored specifically for life science organizations. Our cross-technology expertise helps integrate diverse data sources, clean and prepare complex data for ML modeling and build innovative analytics solutions to deliver faster insights for pharmaceuticals, biotechnology, and life science companies.

Building org-wide capabilities with life science data and analytics services

Reduce operational margins by increasing the efficiency of your entire manufacturing process from procurement to production.

  • Quality control and monitoring
  • Overall equipment effectiveness
  • Master production scheduling
  • Schedule adherence

Data processing, ML model development, and forecasting help optimize inventory planning, scheduling, and overall supply chain costs.

  • Demand forecasting
  • Cost driver analysis
  • Procurement analytics
  • Vendor management

Optimize sales and marketing efforts by powering sales force sizing, territory alignment, call planning, and incentive compensation.

  • Sales forecasting
  • eCommerce analytics for consumer healthcare
  • Marketing attribution
  • Promotion response modeling

What we do

High-volume data integration

Collect, integrate, and analyze high-volume data from diverse sources to create centralized data hubs for commercial business insights.

Granular metric analysis

Perform granular analysis of metrics like average ingredient cost per prescription and drug utilization to increase revenue.

Visualization of complex data

Enable better decision-making with faster reporting and visual insights for research, clinical trials, and healthcare operations.

Customer success stories

Insights and perspectives


How to build a data analytics team for digital business


Why is data validation crucial for long-term data success


Checklist to evaluate a data strategy on the cloud

Looking to drive higher value from your digital and analytics strategies at scale?