## 1 Introduction  
In 2011, the World Economic Forum stated that \"data will be the new oil\" [1]. This metaphor is intended to express not only that the commodity 'data' has a steadily growing economic value [2] but also that this commodity is becoming one of the most important drivers in the industrial world [3]. This change is facilitated in particular by the Internet of Things (IoT). As a result, machines are equipped with sensors to collect data on all kinds of manufacturing aspects [4]. Furthermore, all machines are connected to data processing infrastructures, which continuously analyze the measured data to monitor the manufacturing process and, if necessary, intervene to optimize it [5]. Thus, the comparison of data with oil seems quite apt, as oil was a significant driver for the Technological Revolution in the 19 th century and data are the catalyst of the Fourth Industrial Revolution in the modern age [6]. The World Economic Forum also recognized that their 2011 vision of the future has meanwhile become reality when they concluded in 2017 that \"the world's most valuable resource is no longer oil, but data\" [7]. While the allegory of data and oil primarily invokes associations with industrial application domains--often coined as Industry 4.0 [8]--IoT-based data capturing also enables a general improvement of everyone's daily life. Such data-driven services are often referred to as Smart Living [9]. This umbrella summarizes, e.g., services in the area of Smart Mobility [10], Smart Health [11], and Smart Homes [12]. This also shares some similarities with oil, as the extensive use of oil initially transformed the industry but gradually found its way into the private sector in the form of new types of products and finally changed society as a whole [13]. Data also possess this potential--it is not a coincidence that IoT technologies are seen as a booster for the digital age [14]. Besides their strategic and economic importance, data and oil share another common feature: like oil, data initially have to be refined before they can be used profitably [15]. [PERSON], Senior Vice President of Gartner Research, addresses this fact with his statement that \"information is the oil of the 21 st century, and analytics is the combustion engine\". So, in order to extract interpretable information from raw data, they must first be structured and put into context [16]. This requires processes to systematically transform the data [17] as well as tools and techniques to support such information management [18]. Only when the data have been properly refined can they reach their full potential [19]. While it therefore seems that the metaphorical comparison is sound and that data are just another commodity to be managed and processed like any other asset, a closer look reveals significant differences between the digital commodity 'data' and physical commodities such as oil [20]. These differences are so fundamental that it is necessary to rethink the way data are handled in order to be effective and efficient in the process [21]. In this paper, we therefore study and discuss which characteristics are unique to the intangible commodity 'data' and the resulting implications for modern data administration. To this end, we make the following three contributions:  
1. We discuss the key differences between data and oil. For that purpose, we identify ten specific characteristics of data that need to be taken into account in data administration. In the context of this work, we focus on inherent challenges that arise due to technical characteristics of big data, often referred to as Big Vs [22]. Ethical social aspects, e.g., data liberation and fair distribution [23], or green processing, e.g., energy-efficient data acquisition and storage [24], are out of scope since such factors must also be considered for any other commodity. 2. For each identified special characteristic, we derive the resulting implications for data administration.