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Today, Enterprises act in an increasingly interconnected world and in different kinds of collaborative networks. They are part of business ecosystems in which they interact with their customers, partners and competitors. The processes of analyzing and planning the intertwinement of business and IT architecture within enterprises has been successfully supported by enterprise architecture management (EAM) approaches. In this paper, we analyze four cases from different industries (health care, logistics, retail, and education) and argue that the intra-organizational concepts of enterprise architectures (EA) and EAM need to be extended to grasp the challenges of the enterprises' interconnectedness. Beyond the known concepts of extended enterprise architecture and federated architectures, we define five stages of extended architectures. Additionally, we describe challenges and existing solutions, which are relevant for this extended perspective.

The processing of personal data has evolved into an integral component of businesses by providing several data-driven opportunities. Simultaneously, businesses struggle with the associated responsibility for privacy, as recent data scandals have shown. As a consequence, the European Commission has passed the General Data Protection Regulation (GDPR) to enhance the rights of citizens and the requirements on data protection. This paper argues that enterprise architecture (EA) models can be a key to compliance with the GDPR. Following an incremental research approach, we categorize the major obligations resulting from the GDPR, derive essential stakeholder concerns and outline necessary EA elements for capturing aspects of analytics, security and privacy in EA models. On this basis, a privacydriven EA meta-model is developed that is capable of answering key concerns resulting from the GDPR.

The competition and the collaboration of established banks and challenging fintechs are expected to dramatically change the financial services ecosystem. The different types and roles of fintechs as new niche players in the ecosystem are not well understood so far. However, a better understanding of these types and roles is required for incumbent as well as for new actors for defining and aligning their strategies. In this paper, we analyze the business models of 195 fintech companies with a special focus on the role of data. Based on this analysis, we present a structured overview of fintechs' business areas as well as six data-related business model types. This paper contributes to the research on data-driven business models and business ecosystems by applying and modifying an existing approach for classifying new niche players based on the data dimension of their business models.