Integration of Clinical Research Data
The customer was collecting clinical research data from multiple sources and in various formats, which was making it difficult to integrate and analyze the data. This was leading to data inconsistencies and errors, which was compromising the accuracy and completeness of the study results.
Hence, the customer conducted a due diligence exercise to determine the best mechanism to analyze the clinical research data received from multiple sources.
DiLytics provided the solution for integrating clinical research data collected from multiple source systems into EDW. For this, DiLytics:
- Phase I: Oracle Manufacturing Analytics was installed, set up, and configured and out-of-the-box data was loaded from Oracle E-Business Suite (EBS) and Oracle Advanced Supply Chain Planning (ASCP).
- Phase II: Out-of-the-box Discovery and Requirements Gathering – capture business requirements for use of Manufacturing Analytics by the manufacturing team. Detailed design, development, and testing of Oracle Manufacturing Analytics to implement and deploy the product based on users’ business requirements.
- Phase III: Subsequent rollouts to address requirements not implemented in Phase II.
DiLytics implemented a solution on Oracle Analytics that involved:
- Extended EDW to create new tables and add columns to existing tables
- Developed shell script to move data files from SFTP server location to Data Integration server so that extract, transform, and load process (ETL) can pick up the data from flat files for loading into EDW
- Developed Data Integration layer using Informatica to extract clinical research data from flat files provided by the customer vendors into staging tables
- Developed data validation logic to validate the data in the flat files provided by the customer vendors
- Developed ETL code using Informatica to load clinical research data from the EDW staging tables into the designed data model
- Collaborated with the customer in integrating data from EDW into Board
Key business benefits to the client included:
- Improved decision-making
- Improved accuracy and completeness of clinical research data
- Increased efficiency in the clinical research process
- Superior competitive advantage due to improved collaboration among customer and clinical research data vendors.
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