## 2 Characteristics of Data and Resulting Consequences for Data Administration  
As outlined in the introduction, besides the metaphorical level, there are also many similarities between oil and data when thinking about the handling of these two commodities. For instance, both have first to be discovered and then extracted. The extracted product, i.e., the crude oil or the raw data, must then be refined to transform it into a usable resource (i.e., value-added products such as kerosene or information). For this purpose, the commodity has to be cleansed and preprocessed. The resources refined in this way must then be stored and delivered to customers [25]. While the handling of both commodities involves the same steps, there are considerable differences in the actual implementation. These are due to special characteristics of data that distinguish them significantly from oil. From our point of view, ten characteristics must be taken into account to this end. In the following, we present these characteristics and discuss their consequences for data administration. 1. _Data are nonconsumable:_ When tangible commodities are transformed into value-added products, they are consumed in the process. This is completely different for the intangible commodity 'data'. Even after data have been fully analyzed and a value-added data product has been generated (e.g., information or knowledge), the raw data are still available. They do not lose their meaning in the process, since they can be processed again, in another way, in order to derive new information or knowledge from it. Therefore, the volume of the data to be administered increases constantly, since processed data are neither consumed nor become worthless. For this reason, data management systems are needed that store these volumes of raw data in a resource-efficient manner as well as concepts that enable efficient access to the data. Without such concepts, data can no longer be retrieved as needed, resulting in an economic loss of value. 2. _Data can be duplicated losslessly:_ The supply of tangible commodities is finite. For instance, every single drop of oil is unique and can only be consumed once. Tangible commodities can be thinned down to a certain degree, but this reduces their quality and thus their value. Data, on the other hand, can be duplicated indefinitely and even without any loss. This initially sounds promising to data producers and data providers since it means that their product can never run out. However, this fact also means that the value of data is measured differently than that of oil. While the price of oil is determined primarily by supply and demand, the value of data is determined by how original and unique their content is. If previously unknown correlations can be determined with them, they represent a clear competitive advantage and thus a particularly high value. Whereas the more the data are reproduced--i.e., their content becomes common knowledge--the lower their value becomes. Concepts are therefore needed to ensure that the contents of the data remain as confidential as possible and that only certain data consumers gain insight. 3. _Data are generated at high velocity:_ Tangible commodities are available wherever they arise naturally. For instance, crude oil remains in the Earth's crust until it is extracted. This can be done based on demand and free capacities in the refineries. Data, on the other hand, are generated at any point in time. A processable data object is obtained only if they are captured at exactly this point in time. If they are not captured, they are lost. However, since many IoT devices have limited memory resources, they cannot store the captured data indefinitely but rely on a stream-based processing concept, i.e., they process the data on the fly. Therefore, while oil requires a pull model (i.e., it is acquired from the source when needed), data require a push model (i.e., the source transmits the data when captured).