Secure Data Is FAIR(er) Data
Company Size
1,000+
Country
- Worldwide
Product
- Genedata Profiler
Tech Stack
- Data Management
- Data Security
- Data Integration
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Innovation Output
- Productivity Improvements
Technology Category
- Cybersecurity & Privacy - Database Security
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Life Sciences
- Pharmaceuticals
Applicable Functions
- Product Research & Development
Services
- Data Science Services
- System Integration
About The Customer
The customer in this case study is the biopharmaceutical industry at large. Biopharmaceutical companies are involved in the research, development, and production of pharmaceutical drugs based on biological sources. These companies generate a large amount of R&D data, which is a strategic asset used to build a more accurate and meaningful picture of targets, molecules, patients, and their responses to drugs. The data originates from a variety of sources, including internal databases, external collaborations, and public repositories. The industry is recognizing the value of sharing and reusing data for multiple analyses, which requires breaking down data silos. However, the data generated in biopharmaceutical R&D is complex and requires the implementation of the FAIR principles for scientific data management and stewardship.
The Challenge
Biopharmaceutical companies are facing the challenge of managing and utilizing their R&D data, which is often siloed within different functions of the company. This siloed data structure hinders the effective federation of distributed data, which is crucial for increasing clinical trial success rates. The industry is recognizing the value of sharing and reusing data for multiple analyses, which requires breaking down these data silos. Furthermore, the data generated in biopharmaceutical R&D is complex and originates from various sources, making it crucial to implement the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management and stewardship. However, making data FAIR is not an easy task and requires the right infrastructure that can handle the data volume and privacy of patient-level information.
The Solution
The solution to these challenges is a three-step process. The first step is to break down data silos to build an institutional memory for all data and make data-informed decisions. This can foster early go/no-go decisions, reduce drug development times, and decrease the number of costly late-stage failures. The second step is to make data FAIR. This involves implementing new data management approaches to centralize and massively harmonize the enormous amount of R&D data, which provides human access to high-quality integrated data and digital technologies. The third step is to keep data secure. This is particularly important for clinical trials that must comply with informed consent policies. The Genedata Profiler software solution is used to break down data silos and serve as the single source of truth for all translational and clinical research data, keeping data secure and FAIR.
Operational Impact
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