Learn how to accelerate your research abilities in life sciences and find out how to use virtualized data to drive discoveries and breakthroughs
Translational medicine is an area of research in life sciences that focuses on bringing together early-stage research and development with downstream clinical outcomes to better understand the effects of drugs and therapies for real-world patient outcomes. This area has been considered a ‘holy grail’ of life sciences for decades because solving it effectively means clinical delivery of new medicines from laboratories will be more easily achievable.
To accomplish these outcomes, it is important to be able to utilize a plethora of data from different research and clinical areas. These data sources are normally spread across organizations, exist in incompatible formats and are often inconsistently labeled. This means a wide variety of data sources are required for translational medicine to be effective.
Traditionally, software development in clinical research meant that large amounts of heterogeneous data needed to be migrated together, transformed and mapped into a common repository so complex searches and analytics can be performed.
The first stage of the data migration process normally includes building a pipeline for the data to be extracted, transformed and loaded (ETL’d) into the new system – a process which can take months, or even years, to complete.