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RxDataScience brings a comprehensive suite of products for Biotech and Pharmaceutical companies conducting their Phase 2/3 clinical trials through to commercialization and post-marketing.
Our advanced solutions go beyond basic analytics to include elements of AI, Machine Learning, and Data Science. Experience tells us that companies in healthcare struggle with disparate data sources in different teams & departments. There is a redundancy of data and storage of data in silos, often time leading to analytics that just meets the limited needs of a team for a limited data set.
We work best as an extension of your team where we deliver services and solutions that enable you to achieve your business objectives. Our industry standard platform includes pre-built applications, in the clinical, medical affairs, commercial, HR, finance domains, that can be configured to meet your company specific needs.
RxDataScience Biopharma Analytics Platform, with its Data Lake solution and business domain focused analytics solution, can be your analytics solutions partner in your journey from Phase 2/3 clinical trial to Commercialization as a single source of truth for both data and analytics.
Our advanced analytics solutions and Data Science capabilities will allow for scalability when you most need it, post commercialization, in gaining insights and making informed decisions.
Our proprietary platform uniquely positions us to deliver efficient data-driven commercial strategies to each client’s needs.
Our Data Lake platform “Hubble” provides a single source of truth for all your company's data providing secured access to teams based on their roles.
Combining this with our analytics solution gives a robust end-to-end meta view which your BI team will greatly appreciate.
- Limited platforms in the market place that address life sciences specific requirements e.g. LAAD and Longitudinal patient data.
- Analyzing time series based previous/next actions is not supported in the database layer, but is done programmatically, resulting in performance issues and complexity.
- Weekly data loads for the current RWE & commercial data takes over 48 hours
- Additional 24 hours to complete the ingestion and transformation of 2 TB data
- Significant effort to manage the changes/addition of data sets and business rules
- System not elastic and scalable
- Data from multiple RWE vendors.
- Data kept in original format in silos along with static OMOP conversions
- Utilization is heaviest from SAS against the traditional silo based data source
- Spotty OMOP utilization due to existing code investments