In 2015, Fraunhofer Gesellschaft, business leaders and politicians presented the Industrial Data Space concept aimed at safeguarding the sovereignty of businesses over their own data. The Industrial Data Space focuses on production in industry and is being developed as a foundation for Industry 4.0, supporting primarily the direct exchange of data among business partners. The concept assumes that this exchange is driven by real-time data – partly generated in cyber-physical systems – that are processed in dynamic, self-organizing inter-organizational value networks. As a consequence, data is an important asset, next to labor, goods and services.
In the health care sector and in the life sciences, data is not the primary asset. In the Medical Data Space all activities focus on preserving or restoring the patients' health as effectively and efficiently as possible, on diagnosing diseases earlier and treating them with fewer side-effects, and on identifying new challenges for scientific research and producing robust results. As in the Industrial Data Space, this will be require specific cooperation structures:
- Medical treatment will become a process with increasing inter-sector division of labor, where experts in a network of physicians, nurses, therapists etc. collaborate in healing the patients.
- Due to growing specialization, clinical researchers must collaborate in conducting medical research. The complexity and specificity of their questions as well as requirementsof individualized therapy make it necessary to compile completely novel data sets of groups of patients that may be living in places all over the world.
- Modern drug development requires creating 'real-world evidence' by bringing together the data worlds on drug research and on medical therapy, which are clearly separate until now.
In all these scenarios it is plain to see that data will be key to preventive, personalized, precise and participative health care. However, their full potential can be tapped only by integrating these highly dispersed, heterogeneous data. Whether it is data on therapies, data from clinical research or data generated by cyber-physical medical devices – only integrating, processing and analyzing them will create value. More so than in the Industrial Data Space, these data must be analyzed, combined and annotated in a collaboration of human and artificial intelligence. Obviously, these data collections, which are created to answer temporary questions related to specific issues or fields of study, can be managed effectively only if highly diverse partners work together efficiently.