## 2 Characteristics of Data and Resulting Consequences for Data Administration
Since data currently accumulate at a high velocity, data storage systems must either have large input buffers to temporarily store new data until screening and further processing or have the necessary capacities to handle voluminous data streams. 4. _Data are volatile:_ Oil has no expiration date, which is why processing is not time critical. Yet, data are volatile. Although a data object can be stored indefinitely, its content issometimes only relevant for a very short time. For instance, if a sensor in a driverless car detects that there is an obstacle in the lane, this information must be processed immediately, since it is only relevant until the collision occurs. In other cases, data also become invalid. For instance, if the driverless car detects that a traffic light is green, this information is rendered invalid as soon as the light changes to red. Data storage systems must therefore be able to cope with this limited lifespan and have the capability to process data in (near) real time. While some tangible commodities also have a limited shelf life, the volatility of data dynamically differs from data object to data object, and it is often not possible to specify its expiration date in advance, i.e., how quickly the data must be processed. V. Data are heterogeneous: Tangible commodities are usually homogeneous. Although each drop of oil is unique (i.e., it exists only once), all drops from one source are identical in terms of properties such as purity and quality. Therefore, all extracted oil can be stored in a common container and refined similarly. Data, meanwhile, are heterogeneous. For instance, they can have different data formats or schemata and have different levels of completeness and accuracy. Furthermore, the contents of the data differ. Therefore, data cannot be stored in a common storage. Either all data must initially be transformed into a common structure or data stores that support heterogeneous structures are required. Metadata management is also required to track the properties of the data so that they can be handled appropriately. VI. Data refinement has to be in accordance with the data source and intended use: There are established refinement processes for tangible commodities in order to convert them into certain value-added products. Even though these processes may be adapted over time due to new findings or technical innovations, they can be seen as static processes. With data, this is completely different. On the one hand, new and improved cleansing and preparation techniques are constantly developed, which require adjustments to the data refinement process. On the other hand, due to the heterogeneity of the data, a variety of processing steps geared to the raw data are required. This is aggravated by the fact that there is no one-size-fits-all data refinement process. Rather, adjustments must be made to the steps of the data refinement process depending on the intended use of the data. Only if the process is tailored to both the raw data and the intended use can an optimal result can be achieved. Therefore, data refinement requires flexible adjustments to dynamically respond to changes in sources (i.e., the raw data) and sinks (i.e., the intended use). VII. The economic value of data is uncertain: The value of tangible commodities is generally known. There are some fluctuations due to supply and demand, and over time, commodities can gain or lose value. However, these fluctuations tend to be rather small, while substantial changes are extremely rare. With data, this is completely different. Here, the economic value is initially completely unknown. Data that appear to be worthless today may prove to be needle-movers tomorrow. The reason for this is on the one hand that the derivable knowledge cannot be identified in advance but only when the data have been processed. On the other hand, in such a highly dynamic environment, new use cases for data are constantly emerging, which subsequently define the need and thus the value of the data.