Our Integrated R&D Analytics Platform provides a rich environment for harnessing cross functional data, enabling better insights and decision-making.
Built on Windows Azure, Synapse and other Microsoft SaaS technologies, the platform integrates data from clinical, quality, regulatory, safety and manufacturing within Life Sciences all into one repository that can be mined for analysis and insights. This increases efficiencies by preventing delays, minimizing lost revenues, avoiding penalties, and maintaining company reputation.
If a pharmaceutical drug results in adverse events due to contamination, drug sponsors must identify which markets use that particular manufacturing site. Then sponsors can distribute warnings and recall products from these markets. This requires data from multiple organizational units to be extracted and reconciled for impact analysis and decision making, which is a cumbersome process. Most organizations manage information as silos within each function, so it’s difficult to get a single line of sight on an individual product.
Our platform addresses this data integration challenge by:
- Performing cross-functional impact analysis in a holistic, efficient manner.
- Taking preventive actions to preserve product and brand reputation by implementing effective product recalls around key adverse events or product quality issues.
- Implementing strategies for submissions based on impact analysis and regulatory requirements.
- Saving time and money by preparing for submissions.
- Identifying and integrates structured and unstructured data and content.
- Collecting meaningful, real-time data.
- Synchronizing, transforms, and de-normalizes data.
- Managing high-quality data.
- Identifying relationships between seemingly disparate data sources.
- Providing tools for prescriptive and predictive analytics and other meaningful insights.
- Configurable data ingestion from disparate sources and defining rules for transformation
- Intuitive data representation for effective decision-making
- Leveraging AI/ML algorithms for anomaly detection
- Time series analysis for detecting trends and pattern recognition
- Extraction of key data based on patterns from unstructured content