The majority of commercially available legacy clinical study data conversion solutions are resource intensive. Manual execution steps and poorly executed re-usable components prevent efficient processing and need to be repeated for each conversion endeavour. Data quality and validation tasks are cumbersome and rarely automated, which leads to increased costs. Complete end-to-end workflows and collaboration tools with the sponsor are often non-existent. This prolongs the execution time. Consequently, economies of scale are difficult to achieve as more and more legacy studies are converted. As a result, quality, delivery and functionality issues exist resulting in unmet business needs. .