Regulatory Data Management
New regulations and guidelines demand Life Sciences companies to constantly look at ways for tracking, standardizing and providing the required data in structured format to Health Authorities in order to be compliant. Our qualified RegOrbit expertise team has an extensive experience in Regulatory Data Management which includes
Product Registration & Tracking
- Support Regulatory Information management (RIM) system selection and implementation support
- Assist to develop the full life-cycle of the product registration process and maintain global regulatory compliance
- Manage post submission query raised by regulatory authorities
- Graphical dashboard and internal reporting workflows
Data gap analysis
- Analyse existing data flow and allow your business to be aware of the nature of your data utilization
- Identify the reason/s why you are producing and using data that is either enough or lacking in accordance to the needs of your business
- Support business to identify the areas where it excels as well as the processes and programs where particular data improvements are necessary
Data cleansing
- Identifying and removing duplicate regulatory records
- Identifying and revising irrelevant, inaccurate, incomplete, missing, spurious, invalid, corrupt or obsolete regulatory data
- Data auditing and aggregation
- Identifying key variables in an existing regulatory database
- Suggesting new variables to enrich a regulatory database
- Interlinking and consolidating multiple regulatory data sources
Data Analytics & Reporting
- Support users to focus on exploring and analyzing regulatory data, a high-value activity
- Provide business value from data insight
- Support to configure data analytic solution
- Data ingestion and integration
- Help to implement transformative data visualization and reporting capabilities (Power BI, Spotfire, Qlik View)
- Provide recommendations for optimized performance and better reporting
Data Standards & Data Governance
- Help to ensure that your regulatory & R&D data are consistent and reliable
- Methodically standardize data terms, phrases as well as specific attributes within the dataset, regardless of the volume and complexity of data’s
- Provide the opportunity to develop an understanding of data’s value and meaning
- Support to develop Data Governance model to oversee and manage of how data is captured, stored, aligned and used.