Leveraging data virtualization, drug development teams and partners can create a sort of virtual data layer; a logical or canonical view of entities from disparate, structured, semi-structured or unstructured data sources. In the case of sample intelligence, the entities are samples, patients, informed consents, the type of disease, inhibitor or molecule, or any other relevant descriptive data. Once that virtual data layer is created, it creates unifi ed access to the intelligence which can be served up to users in innovative visual ways. This makes it easier for scientists and clinical researchers to achieve real-time or right-time access to the data intelligence that is located across distributed or disparate data sources. This valuable access also signifi cantly reduces the need to constantly replicate the information, which saves time and cost, and establishes an easier path to governance by applying security and access rules. This approach also supports US FDA 21CFR Part 11 compliance requirements for regulated environments.

data virtualization

Conceptual Data Virtualization Diagram

Data Harmonization & Virtualization

Bringing structured data, internal unstructured content systems, and external data that has structure but is not easily accessible together has never been more important for sample management data consumers and research organizations looking for information related to samples. Equally important is that data scientists and clinical researchers need consistent visibility into, and access to, all data assets. Presentation of data in a way that is easily accessed and highly usable is critical for clinical researchers and data consumers.

The insight and analysis that is supported by data virtualization reduces research costs and time-to-market for research deliverables. Research organizations can improve their overall global sample data integration; create real-time data visibility and access; easily connect to bioprocessing data; track sample consent; provide intuitive search and discovery of critical.

Sample Intelligence through Data Virtualization

Bringing structured data, internal unstructured content systems, and external data that has structure but is not easily accessible together has never been more important for sample management data consumers and research organizations looking for information related to samples. Equally important is that data scientists and clinical researchers need consistent visibility into, and access to, all data assets. Presentation of data in a way that is easily accessed and highly usable is critical for clinical researchers and data consumers.