# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 1 Introduction  
As a global network that enables real-world assets to interconnect, intercommunicate, and interact digitally, the Internet of Things (IoT) is of interest for all industrial domains that deal with physical goods, where manufacturing is prime. IoT technologies allow organizations to automatically generate digital representations of physical objects that can capture their real-world identities, locations, and states (digital objects). The collection, integration, and analysis of such data enable companies to implement new types of services [1]. Examples documented in the literature include predictive maintenance services for machinery and equipment, process mining for determining bottlenecks in complex production environments, fine-tuning of recipes and sequences for process manufacturing [2], optimization of plant availability, energy management, monitoring and analysis of production and logistics processes [3], and new approaches to the management of packaging [4]. The context of most of these scenarios is not an isolated workplace or an individual asset but an environment with a variety of components embedded in large processes. The necessary data for such services can quickly cross company borders [5]. Data are often owned by a multitude of organizations, such as the manufacturing company, provider of (smart) machinery, suppliers of parts and materials, operator of smart sensors, logistics and transport enterprises, possible customers, and more. This entails the necessity for those organizations to cooperate to design innovative, analytics-based business models, such as machinery as a service, production on demand, or asset benchmarking [6, 7]. In the academic literature, respective collaborations are discussed under the subject of IoT ecosystems [5]. An IoT ecosystem denotes a community of interacting firms that compete and cooperate using a common set of core assets and digital objects [5]. To date, viable IoT ecosystems have predominantly been created around a focal company that usually also acts as a platform provider and introduces financial, technological, and human resources for state-of-the-art data collection and analytics [8]. The advantage of a large and powerful focal company is that it can represent entire value chains and enforce IoT data provision by leveraging its dominant position [9]. The unprecedented power of such players is a relevant (and justifiable) acceptance barrier to enter such constellations-especially for small and medium-sized enterprises (SMEs). Many SMEs, particularly those active in high-tech manufacturing markets, rely on keeping a technological or a business edge that can be easily lost when critical data get into the wrong hands [10]. To counter the platform providers' dominance, it has been proposed to include independent data trustees or platform-neutral \"data spaces\" [11]. Such entities increase reliability and reduce reticence [12]. However, even when neglecting the fact that in doing so, they introduce a level of indirection and complexity that is not conducive to easy implementation of a cross-border analytics scenario for SMEs, they cannot erase the reality of the platform provider remaining in a unique position of power [8]. For these reasons, data sharing applications are a niche segment [13]. An alternative approach is the formation of an IoT ecosystem for SMEs without direct or indirect dependency on a focal company. To facilitate this, it has been proposed to coalesce ecosystems around well-defined and commonly pursued core value propositions in which the capabilities of the various partners are combined [14]. This scenario also entails a rationale for pooling shared resources (i.e., the data and resources needed to implement the IoT analytics solutions) [15] in a joint unit owned by the ecosystem- and not just owned by a single entity of the network or an external enterprise.