In clinical research and development, data is central to optimizing clinical trials, especially the way the studies are designed and executed. In order to meet the demand of scientists, operations, and project management teams, life sciences companies have evolved their data collection systems with cloud-based solutions.
Even though traditional ETL is useful when preparing data for use in business-critical applications, it often falls short of providing information in time. For this reason, novel Clinical Data Lakes (CDL) have been designed to store, cleanse, and harmonize data rapidly and specifically for the type of study design. Using parameterized and repeatable data pipelines in a metadata-driven approach to CDL can help scale data lakes by rapidly integrating data from a variety of study designs and enabling more use cases, without having to rebuild data pipelines or redesign data models.