## 3 Related Work
### Lessons Learned  
As illustrated in Section 2, there are ten key differences between data and tangible commodities such as oil that make them special assets. These special characteristics must also be taken into account when handling and managing data in order to use this commodity effectively and efficiently. Our review of the state of the art shows that there are many research approaches to this end. They can be divided into three categories: In the area of data administration, methods and techniques are being explored that enable or facilitate data acquisition, data preparation, data management, and data provisioning. Thereby, the special challenges in today's big data context (namely, volume, variety, and velocity) are addressed. In the area of data security, approaches are explored that ensure the confidentiality, integrity, and authenticity of the data in order to verify their veracity and thus preserve their value. Furthermore, methods and techniques are developed that guarantee high availability of the data and ensure that only authorized entities have access to them. Data privacy is a branch of data security that plays an important role currently due to the increasing importance of personal data. In the context of this work, we focus on technical issues, whereas legal, ethical, and organizational research is out of scope. In this regard, there are approaches that provide data subjects with more information regarding the processing of their data as well as identifying potential privacy threats. Other approaches address how data processing can be restricted in a fine-grained manner. PET ensure that no sensitive knowledge can be gained from the data without compromising the overall data quality. Finally, methods are being developed to erase data permanently to ensure that they can no longer be processed. In addition to the actual data, this also includes all replicas and metadata as well as all data products that have been created based on those data. However, all of these research approaches are island solutions to individual data administration, security, and privacy problems. Yet, these three aspects are highly interrelated. For instance, a data marketplace cannot provide accountability for data if their origin cannot be traced through provenance and authentication of the sources, while privacy can only be guaranteed if confidentiality is ensured as well. Moreover, data administration can only be effective if availability is ensured. Thus, these aspects must not be considered independently of each other. Even within the individual research areas, the isolated approach represents a disadvantage. For instance, obfuscation techniques for users are immensely powerful because they give data subjects full control over their data. With statistical disclosure control for mass data providers, they lose this control. However, due to the holistic view on all available data, data providers are able to apply privacy techniques in a much more target-oriented manner. Therefore, the optimal solution would be a mutually coordinated mix that first provides data subjects with a prefiltering option and then allows data providers to make comprehensive readjustments from their side. Island solutions cannot achieve synergy effects and, even worse, some of them are mutually exclusive. For instance, privacy can easily be achieved by completely randomizing all data. This, however, minimizes data quality and thus renders the data worthless. Another example is the use of blockchain-based data stores to ensure integrity and availability. Yet, immutable data storages inevitably prevent data subjects from exercising their right to be forgotten. Although these individual solutions are efficient for the respective application context, a holistic end-to-end view from data acquisition to data delivery is required to enable trustworthy and demand-driven data provisioning. 'Trustworthy' refers to enabling data producers to make their data available without losing control over them and without having to fully disclose their assets.